--- title: GeoGLUE canonical_url: "https://www.modelscope.cn/datasets/damo/GeoGLUE" md_url: "https://www.modelscope.cn/datasets/damo/GeoGLUE.md" repository: damo/GeoGLUE chinese_name: "地理语义理解能力评测基准" last_updated: 2023-05-16 license: "Apache License 2.0" storage_size: "2.5 GB" downloads: 46206 stars: 49 --- # GeoGLUE > GeoGLUE - damo 在 ModelScope 开源的数据集。地理语义理解能力评测基准 GeoGLUE（GeoGraphic Language Understanding Evaluation）是由阿里巴巴达摩院自然语言处理组与高德联合发起提供的数据集，旨在推动地理相关文本处理技术和社区的发展。地理文本即描述地理实体、位置的自然文本，具有表达方式丰富、蕴含空间推理、知识强依赖等特点。此外，地理文本与现实地理世界的关联性也带来了诸多挑战。地理文本信息的自动化处理是许多地理语义应用的核心技术，本榜单提炼了其中… damo/GeoGLUE 是 ModelScope 魔搭社区上的数据集，存储大小 2.5 GB，采用 Apache License 2.0 许可。 - **Repository**: damo/GeoGLUE - **License**: Apache License 2.0 - **Storage size**: 2.5 GB - **Downloads**: 46206 - **Stars**: 49 - **Last updated**: 2023-05-16 Source: https://www.modelscope.cn/datasets/damo/GeoGLUE --- ## 快速传送 - [点我试用训练好的门址地址要素解析模型](https://modelscope.cn/models/damo/mgeo_geographic_elements_tagging_chinese_base/summary) - [点我试用训练好的地理实体对齐模型](https://modelscope.cn/models/damo/mgeo_geographic_entity_alignment_chinese_base/summary) - [点我试用训练好的Query-POI相关性排序](https://modelscope.cn/models/damo/mgeo_geographic_textual_similarity_rerank_chinese_base/summary) - [点我试用训练好的地址Query成分分析模型](https://modelscope.cn/models/damo/mgeo_geographic_composition_analysis_chinese_base/summary) - [点我试用训练好的WhereWhat切分模型](https://modelscope.cn/models/damo/mgeo_geographic_where_what_cut_chinese_base/summary) - [点我查看海量地址处理应用案例源码](https://github.com/PhantomGrapes/MGeoExample) ## 数据集简介 地理语义理解能力评测基准 GeoGLUE（GeoGraphic Language Understanding Evaluation）是由阿里巴巴达摩院自然语言处理组与高德联合发起提供的数据集，旨在推动地理相关文本处理技术和社区的发展。 地理文本即描述地理实体、位置的自然文本，具有表达方式丰富、蕴含空间推理、知识强依赖等特点。此外，地理文本与现实地理世界的关联性也带来了诸多挑战。地理文本信息的自动化处理是许多地理语义应用的核心技术，本榜单提炼了其中多个典型场景：地图搜索、电商物流、政府登记、金融交通，并设计了六个核心任务：门址地址要素解析、地理实体对齐、Query-POI库召回、Query-POI相关性排序、地址Query成分分析、WhereWhat切分。为了避免地理信息安全问题，涉及POI地址库部分我们基于全开源GIS系统OpenStreetMap标注，并人工编写了数十万Query。 欢迎业界和学术界的同行们一起加入到GeoGLUE benchmark的建设中，一起来推动地理语义标准化数据集的发展。 ## Release Note - 2023.01.16 数据集发布 ## 数据集加载方式 使用 git clone方式下载resources中的底库数据 ```python git clone https://www.modelscope.cn/datasets/damo/GeoGLUE.git ``` 使用sdk加载 ```python from modelscope.msdatasets import MsDataset # Load the geoeta dataset train_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoETA', split='train') eval_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoETA', split='validation') ``` ## 一键运行baseline 详见[MGeo多任务多模态地址预训练底座](https://modelscope.cn/models/damo/mgeo_backbone_chinese_base/summary) ## 数据及结构 ### 门址地址要素解析 #### 任务描述 常见的门址地址作为寄递或外卖服务中用户位置的表达，一般包含以下几类信息： - 行政区划信息，如省、市、县、乡镇信息; - 路网信息，如路名，路号，道路设施等; - 详细地理信息，如POI (兴趣点)、楼栋号、户室号等; - 非地理信息，如补充说明，误输入等; 本任务需要对将门址地址拆分成独立语义的要素，并对这些要素进行类型识别。 #### 任务说明 - 输入：浙江省杭州市余杭区五常街道文一西路969号淘宝城5号楼，放前台 - 输出：province=浙江省 city=杭州市 district=余杭区 town=五常街道 road=文一西路road_number=969号 poi=淘宝城 house_number=5号楼 other=，放前台 #### 评测指标 我们通过将输出结果与人工标注的集合进行比较来分别计算每一种元素准确率(Precision)，召回率(Recall)和F-1分值(F-1 score)，并采用Micro-F1作为最终排名指标。 具体计算过程如下所示（以元素$T$为例）： 给定一条地理文本输入n，此中有x个T元素： G_n={g_1,g_2,g_3…g_x}，系统输出标注结果中有y个T元素如下：P_n={p_1,p_2,p_3…p_y}，则实体标注的准确率定义如下： ![F1计算](fig/f1_1.png) 实体标注的召回率定义如下： ![F1计算](fig/f1_2.png) 实体标注的F1值定义如下： ![F1计算](fig/f1_2.png) 其中N为整个测试集。 #### 评测数据 本评测开放训练集数据数量8856，验证集1970数量，测试集数量50000。 数据集名称为：GeoETA（Geographic Elements TAgging）。 点击下载[标注规范说明文件](https://modelscope.cn/api/v1/datasets/damo/GeoGLUE/repo/files?Revision=master&FilePath=resources/%E4%B8%AD%E6%96%87%E5%9C%B0%E5%9D%80%E8%A6%81%E7%B4%A0%E8%A7%A3%E6%9E%90%E6%A0%87%E6%B3%A8%E8%A7%84%E8%8C%83.pdf) 其中训练集与验证集包含输入文本与标注结果。每一行为一条标注结果，包括tokens和ner_tags信息。tokens为输入文本字符列表，按照字粒度切分；ner_tags为标签列表，按照BIEOS规范标记。样例如下： ```json {"tokens": ["浙", "江", "杭", "州", "市", "江", "干", "区", "九", "堡", "镇", "三", "村", "村", "一", "区"], "ner_tags": ["B-prov", "E-prov", "B-city", "I-city", "E-city", "B-district", "I-district", "E-district", "B-town", "I-town", "E-town", "B-community", "I-community", "E-community", "B-poi", "E-poi"]} ``` 训练与开发集加载方式为： ```python from modelscope.msdatasets import MsDataset # Load the afqmc dataset train_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoETA', split='train') eval_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoETA', split='validation') ``` 测试集为需要选手做预测的文件，每一行为一条需要预测的文本及其idx。样例如下： ``` {"idx": "1", "tokens": ["朝", "阳", "区", "小", "关", "北", "里", "0", "0", "0", "-", "0", "号"]} ``` 选手需要对测试集做预测，输出测试结果。测试结果每行为一条预测，括idx、tokens和ner_tags信息。样例如下： ``` {"idx": "1", "tokens": ["浙", "江", "杭", "州", "市", "江", "干", "区", "九", "堡", "镇", "三", "村", "村", "一", "区"], "ner_tags": ["B-prov", "E-prov", "B-city", "I-city", "E-city", "B-district", "I-district", "E-district", "B-town", "I-town", "E-town", "B-community", "I-community", "E-community", "B-poi", "E-poi"]} ``` ### 地理实体对齐 #### 任务描述 日常生活中输入的地理文本可以为以下几种形式： - 包含四级行政区划及路名路号POI的规范地理文本； - 要素缺省的规范地理文本，例：只有路名+路号、只有POI； - 非规范的地理文本、口语化的地理信息描述，例：阿里西溪园区东门旁亲橙里； 每一种地理文本的表述都可以映射到现实地理世界的道路、村庄、POI等实体上。地理实体对齐任务需要判断两段地理文本是否指代同一地理实体。该任务是构建地理信息知识库的核心技术。 #### 任务说明 多样化的地理文本写法对地理实体对齐任务提出的挑战如下： - 同一个地理元素存在多种写法，没有给定的改写词表； - 地理文本一般存在省市区等限制条件，需要结合限制条件分析相关性； - 不同地市地理文本描述规范不一，对模型泛化性提出更高要求； 输入：输入文件包含若干个地理文本对 输出：输出文本每一行包括此文本对的对齐程度，分为完全对齐（exact_match）、部分对齐（partial_match）、不对齐（not_match） #### 评测指标 针对3个对齐程度标签，计算macro F1指标： ![Macro-F1计算](fig/f2.png) #### 评测数据 本评测开放训练集数据数量96423，验证集数量22974w，测试集数量35908。 数据集名称为： GeoEAG（Geographic Entity AliGnment）。 点击下载[标注规范说明文件](https://modelscope.cn/api/v1/datasets/damo/GeoGLUE/repo?Revision=master&FilePath=resources/%E5%9C%B0%E5%9D%80%E5%AE%9E%E4%BD%93%E5%AF%B9%E9%BD%90%E6%A0%87%E6%B3%A8%E8%A7%84%E8%8C%83.txt) 其中训练集与验证集包含输入文本与标注结果。每一行为一条标注结果，包括sentence1和sentence2以及label信息。sentence1、sentence2为输入的地址文本对；label对齐程度标注。样例如下： ```json {"sentence1": "红椿路38-3号12号楼", "sentence2": "红椿路38-32号保利·茉莉公馆", "label": "partial_match"} ``` 训练与开发集加载方式为： ```python from modelscope.msdatasets import MsDataset # Load the afqmc dataset train_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoEAG', split='train') eval_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoEAG', split='validation') ``` 测试集为需要选手做预测的文件，每一行为一条需要预测的文本及其idx。样例如下： ``` {"sentence1": "海尔路199号海语江山16栋", "sentence2": "江北区阳明山水16栋", "idx": "1"} ``` 选手需要对测试集做预测，输出测试结果。测试结果每行为一条预测，括idx、tokens和ner_tags信息。样例如下： ``` {"idx": "1", "sentence1": "苏家屯区月季街9号楼", "sentence2": "苏家屯区萃华园9号楼", "label": "not_match"} ``` ### Query-POI库召回（本任务数据集后面随GeoGLUE榜单一起发布） #### 任务描述 POI（Point Of Interest，兴趣点）搜索是地图类应用的核心功能，需要根据用户的query找到对应地图上的POI。一个POI的组成通常是其对应的地址描述以及经纬度描述。常见的POI数据库从几百万到几亿不等，从海量的POI库中召回用户所想的地点是个富有挑战性的课题。 #### 任务说明 任务会给出一个POI库，包含海量POI的地址描述以及经纬度（wgs84坐标系）。需要为每一条query在POI库中找到其对应的POI。数据保证每一个query在POI库中一定有唯一正确答案。 输入：用户query+用户位置 输出：POI库里的正确POI id #### 评测指标 MRR@5：MRR是一个国际上通用的对搜索算法进行评价的机制，其评估假设是基于唯一的一个相关结果，即第一个结果匹配，分数为 1 ，第二个匹配分数为 0.5，第 n 个匹配分数为 1/n，如果没有匹配的句子分数为0。最终的分数为所有得分之和。 @5表示只看前五个排序结果，假如正确答案排在第六个及以后则计0。 #### 评测数据 本评测开放训练集数据数量50000，验证集数量20000，测试集30002 数据集名称为： GeoTES-recall（Geographic TExtual Similarity）。 POI库每一行为一个POI，doc_id为POI ID，address为POI地址描述，gis为该POI的经纬度以及该经纬度在OpenStreetMap中查询到的gis信息所有的gis信息都是长度为6的列表（需要再json.loads一次），分别表示OpenStreetMap中与该经纬度有一定关系的几何体，列表依次包含这些几何体的ID信息、几何体类型（3表示AOI，4表示road）、经纬度与几何体的关系（3表示经纬度包含在几何体里面，4表示经纬度在几何体附近200m以内）、几何体左下角与右上角位置（4位数字，离散化）、经纬度离几何体左下角和右上角的占比（4位数字，离散化）、POI经纬度样例如下： ```json {"doc_id": 2633089, "address": "杭州市唐栋大桥隔里", "gis": "[[85462, 85463, 85468, 85469, 85472, 85473], [4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4], [[622, 736, 626, 764], [622, 736, 626, 764], [615, 762, 626, 766], [615, 762, 626, 766], [626, 764, 631, 770], [626, 764, 631, 770]], [[33, 33, 23, 33], [33, 33, 23, 33], [33, 30, 23, 16], [33, 30, 23, 16], [23, 23, 14, 23], [23, 23, 14, 23]], \"119.06179021286454,29.638503988733607\"]"} ``` 我们通过这个例子再次理解一下gis信息的表达。在这个例子中，POI为"杭州市唐栋大桥隔里"，所在位置是"119.06179021286454,29.638503988733607"，这个位置附近在OpenStreetMap中有以下几何体[85462, 85463, 85468, 85469, 85472, 85473]。这些几何体分别是都是道路，因此类型编码都是4。这个位置在这些几何体的附近，没有一个几何体包含该位置，因此经纬度与几何体的关系都是4。接下来的两个list分别是上面提到这些几何体的左下角与右上角位置（4位数字，离散化）以及经纬度离几何体左下角和右上角的占比（4位数字，离散化）。 训练集与验证集包含用户query，pos_id为正确的POI id以及用户位置的gis信息query_gis。每一行为一条标注结果，格式为json列表。样例如下： ```json {"query": "学院路与天目山路辅路交叉口北100密大厦接送点", "pos_id": 1878520, "query_gis": "[[34441, 34442, 34449, 34450, 34451, 47490, 48183, 48196, 48213, 48214, 48218, 48220, 48253, 48254, 48255], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1230, 1312, 1232, 1313], [1231, 1312, 1233, 1312], [1231, 1310, 1234, 1312], [1233, 1312, 1233, 1312], [1232, 1312, 1233, 1312], [1230, 1310, 1231, 1312], [1231, 1311, 1231, 1312], [1231, 1311, 1231, 1312], [1231, 1311, 1232, 1312], [1231, 1312, 1232, 1312], [1232, 1311, 1232, 1311], [1232, 1311, 1232, 1312], [1232, 1311, 1233, 1311], [1232, 1311, 1233, 1312], [1232, 1311, 1233, 1312]], [[33, 35, 23, 35], [28, 43, 19, 43], [24, 37, 15, 37], [3, 43, 3, 43], [12, 43, 3, 43], [35, 37, 25, 37], [41, 43, 31, 43], [40, 43, 30, 43], [37, 40, 27, 40], [35, 43, 25, 43], [43, 43, 40, 43], [43, 43, 35, 43], [23, 43, 13, 43], [23, 43, 13, 43], [23, 43, 14, 43]], \"120.05198248678437,30.18322538622843\"]"} ``` 测试集为需要选手做预测的文件，每一行包括数据编码idx、用户query以及用户位置的gis信息query_gis。样例如下： ``` {"idx": "1", "query": "永晖路与济仁路三岔路燃气公司", "query_gis": "[[38213, 38214, 38230, 67079, 70838], [3, 3, 3, 4, 4], [4, 4, 4, 4, 4], [[1358, 1335, 1359, 1336], [1359, 1335, 1360, 1336], [1359, 1337, 1360, 1339], [1337, 1328, 1500, 1459], [1337, 1328, 1415, 1359]], [[33, 43, 23, 43], [23, 43, 13, 43], [19, 28, 9, 18], [24, 23, 15, 23], [25, 26, 16, 20]], \"120.25945287208994,30.20803020716775\"]"} ``` 选手需要对测试集做预测，输出测试结果。测试结果每行为一条预测，包括数据编码idx、用户query、用户位置的gis信息query_gis以及正确的POI id pos_id。样例如下： ``` {"idx": "1", "query": "永晖路与济仁路三岔路燃气公司", "pos_id": 30322, , "query_gis": "[[38213, 38214, 38230, 67079, 70838], [3, 3, 3, 4, 4], [4, 4, 4, 4, 4], [[1358, 1335, 1359, 1336], [1359, 1335, 1360, 1336], [1359, 1337, 1360, 1339], [1337, 1328, 1500, 1459], [1337, 1328, 1415, 1359]], [[33, 43, 23, 43], [23, 43, 13, 43], [19, 28, 9, 18], [24, 23, 15, 23], [25, 26, 16, 20]], \"120.25945287208994,30.20803020716775\"]"} ``` ### Query-POI相关性排序 #### 任务描述 POI（Point Of Interest，兴趣点）搜索是地图类应用的核心功能，需要根据用户的query找到对应地图上的POI。一个POI的组成通常是其对应的地址描述以及经纬度描述。本任务需要在给定用户query以及候选POI列表的情况下，根据POI与query的相关性程度对POI进行排序。 #### 任务说明 任务的每一条输入包括用户query、用户位置以及候选POI列表，每个POI包括POI的地址描述以及POI位置。需要根据query与POI的相关性按照相关度从高到底为POI打分，输出得分最高的POI。 输入：用户query、用户位置、候选POI列表 输出：得分最高的POI #### 评测指标 MRR@1：对于一条query，如正确答案被排在第1位，则算正确，否则错误。计算最终的正确率即为MRR@1。 #### 评测数据 本评测开放训练集数据数量50000，验证集数量20000，测试集30002 数据集名称为： GeoTES-rerank（Geographic TExtual Similarity）。 其中训练集与验证集包含输入文本与标注结果。每一行为一条标注结果，query是用户查询query，query_gis是包括用户经纬度以及用户经纬度在OpenStreetMap中查询到的gis信息 positive_passages是正例POI列表，长度为1 negative_passages是负例POI列表，训练集的长度为19，验证集的长度为39 所有POI包括两个属性：text是POI地址描述，gis是POI经纬度以及该经纬度在OpenStreetMap中查询到的gis信息 所有的gis信息都是长度为6的列表（需要再json.loads一次），分表表示OpenStreetMap中与该经纬度有一定关系的几何体，列表依次包含这些几何体的ID信息、几何体类型（3表示AOI，4表示road）、经纬度与几何体的关系（3表示经纬度包含在几何体里面，4表示经纬度在几何体附近200m以内）、几何体左下角与右上角位置（4位数字，离散化）、经纬度离几何体左下角和右上角的占比（4位数字，离散化）、POI经纬度 注意：gis信息仅供参考，不强制使用 ```json {"query": "龙轩大厦(装修中)附近地铁站台B2口", "query_gis": "[[], [], [], [], [], \"119.7474200064974,30.428317971898405\"]", "positive_passages": [{"text": "6号线科海路地铁站B2口", "gis": "[[46889, 46998, 46999, 47001, 47058, 47060, 47061, 47062, 47065, 80465, 80466, 80476, 80477, 80482, 80483], [3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1248, 1258, 1250, 1261], [1249, 1260, 1249, 1261], [1249, 1260, 1249, 1261], [1249, 1261, 1249, 1261], [1249, 1254, 1256, 1260], [1249, 1260, 1250, 1261], [1250, 1261, 1250, 1262], [1250, 1260, 1251, 1262], [1251, 1259, 1254, 1264], [1242, 1245, 1251, 1283], [1242, 1245, 1251, 1283], [1249, 1254, 1250, 1260], [1249, 1254, 1250, 1260], [1250, 1260, 1252, 1271], [1250, 1260, 1254, 1275]], [[35, 32, 25, 14], [43, 38, 43, 38], [43, 32, 43, 14], [43, 35, 43, 35], [24, 34, 15, 34], [38, 30, 28, 16], [24, 26, 15, 20], [25, 26, 16, 20], [21, 26, 11, 20], [32, 27, 23, 19], [32, 27, 23, 19], [34, 35, 24, 35], [34, 35, 24, 35], [23, 24, 14, 22], [23, 23, 14, 23]], \"120.08195269974871,30.131676658171365\"]"}], "negative_passages": [{"text": "宝平线龙城公园地铁站-B口", "gis": "[[], [], [], [], [], \"120.32476390302098,30.19117527888314\"]"}, {"text": "北天门大街龙城公园地铁站-B口", "gis": "[[8016, 8018, 8021, 8024, 8049, 73313, 73314, 73315], [3, 3, 3, 3, 3, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4], [[1304, 1520, 1305, 1522], [1304, 1520, 1305, 1521], [1303, 1521, 1304, 1522], [1305, 1522, 1306, 1523], [1302, 1520, 1305, 1530], [1305, 1519, 1306, 1524], [1305, 1519, 1306, 1524], [1298, 1520, 1315, 1524]], [[27, 33, 18, 33], [35, 35, 25, 35], [38, 27, 28, 19], [21, 23, 11, 23], [32, 24, 23, 22], [22, 28, 12, 18], [21, 28, 11, 18], [27, 27, 18, 19]], \"120.17139007733218,30.390874321261478\"]"}, {"text": "常合高速187号中山路地铁站B口", "gis": "[[58051, 58043, 58044, 58045, 58050, 58052, 58053, 58056, 58057, 58058, 76583, 78069, 78080], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1328, 1383, 1330, 1385], [1328, 1382, 1329, 1384], [1328, 1383, 1329, 1383], [1328, 1381, 1330, 1384], [1328, 1384, 1328, 1386], [1328, 1385, 1328, 1386], [1328, 1386, 1328, 1386], [1329, 1385, 1330, 1386], [1330, 1386, 1330, 1386], [1329, 1386, 1329, 1386], [1327, 1378, 1334, 1392], [1287, 1288, 1330, 1389], [1287, 1309, 1330, 1385]], [[27, 28, 18, 18], [32, 35, 23, 35], [35, 43, 25, 43], [27, 36, 18, 36], [43, 23, 33, 23], [43, 10, 35, 10], [43, 3, 42, 3], [23, 23, 13, 23], [9, 3, 3, 3], [23, 3, 13, 3], [26, 27, 17, 19], [32, 32, 23, 14], [32, 32, 23, 14]], \"120.21090697468307,30.254322624809543\"]"}, {"text": "8号线文海南路地铁站B口", "gis": "[[1604, 1624, 1657, 1714, 1734, 1751, 1807, 1816, 1817, 1820, 1828, 2969, 1816, 3064, 3065, 3066, 66103, 66104, 67735, 67738, 67746, 67747, 67760, 67761, 69030, 69031, 69091, 69092, 69093, 69094, 69095, 69096, 69097, 69098, 69099, 69100, 69101, 69102, 69103, 69104], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1426, 1445, 1426, 1445], [1427, 1445, 1427, 1446], [1426, 1445, 1426, 1445], [1426, 1445, 1427, 1445], [1427, 1444, 1428, 1444], [1427, 1444, 1428, 1444], [1426, 1444, 1427, 1445], [1427, 1439, 1431, 1443], [1428, 1444, 1430, 1447], [1426, 1444, 1428, 1448], [1422, 1444, 1428, 1448], [1425, 1440, 1427, 1443], [1427, 1439, 1431, 1443], [1425, 1442, 1426, 1443], [1426, 1443, 1427, 1443], [1426, 1443, 1428, 1444], [1424, 1430, 1496, 1444], [1424, 1430, 1496, 1444], [1427, 1439, 1427, 1444], [1427, 1439, 1427, 1444], [1428, 1444, 1429, 1449], [1427, 1444, 1428, 1449], [1427, 1443, 1434, 1457], [1427, 1443, 1434, 1457], [1367, 1420, 1431, 1444], [1369, 1420, 1431, 1444], [1421, 1444, 1427, 1444], [1421, 1444, 1427, 1444], [1424, 1444, 1427, 1444], [1424, 1444, 1427, 1444], [1426, 1444, 1426, 1444], [1426, 1444, 1426, 1444], [1427, 1444, 1427, 1444], [1426, 1444, 1427, 1444], [1427, 1444, 1427, 1444], [1427, 1444, 1427, 1444], [1427, 1444, 1427, 1444], [1427, 1444, 1428, 1444], [1427, 1444, 1431, 1444], [1427, 1444, 1431, 1444]], [[43, 3, 43, 3], [26, 3, 17, 3], [43, 4, 43, 4], [32, 3, 23, 3], [19, 42, 9, 42], [22, 26, 12, 20], [27, 18, 18, 18], [21, 33, 11, 33], [18, 22, 8, 22], [26, 23, 17, 23], [30, 23, 21, 23], [30, 37, 21, 37], [21, 33, 11, 33], [35, 36, 25, 36], [26, 40, 17, 40], [26, 30, 17, 16], [23, 33, 14, 33], [23, 32, 14, 14], [3, 33, 3, 33], [3, 33, 3, 33], [15, 22, 5, 22], [15, 22, 5, 22], [23, 23, 13, 23], [23, 23, 13, 23], [32, 33, 23, 33], [32, 32, 23, 14], [31, 43, 22, 43], [31, 43, 22, 43], [32, 31, 23, 15], [32, 15, 23, 15], [39, 25, 29, 21], [39, 25, 29, 21], [3, 43, 3, 43], [32, 43, 23, 43], [3, 27, 3, 19], [3, 27, 3, 19], [3, 17, 3, 17], [3, 18, 3, 18], [22, 43, 12, 43], [21, 43, 11, 43]], \"120.3706347107584,30.31346978511931\"]"}, {"text": "2号线人民路地铁站B口", "gis": "[[38913, 38978, 38994, 39023, 69654, 70256, 71010, 71047, 81488], [3, 3, 3, 3, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4], [[1361, 1291, 1361, 1292], [1360, 1289, 1361, 1290], [1361, 1290, 1361, 1292], [1361, 1288, 1363, 1290], [1326, 1254, 1362, 1389], [1326, 1254, 1362, 1389], [1361, 1283, 1361, 1294], [1358, 1292, 1375, 1292], [1361, 1290, 1361, 1291]], [[5, 14, 3, 14], [31, 31, 22, 15], [24, 23, 15, 23], [22, 34, 12, 34], [32, 25, 23, 21], [32, 25, 23, 21], [3, 29, 3, 17], [24, 3, 15, 3], [9, 21, 3, 21]], \"120.26271289585489,30.16073824610953\"]"}, {"text": "1号线金沙湖地铁站B口", "gis": "[[4684, 4663, 4679, 4680, 4682, 4683, 4685, 4687, 4884, 4885, 4886, 69030, 69031, 69055, 69056, 69173, 69174, 69192, 69193, 69194, 69195], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1396, 1441, 1397, 1443], [1387, 1438, 1396, 1443], [1394, 1442, 1396, 1443], [1394, 1443, 1395, 1444], [1395, 1442, 1396, 1443], [1396, 1442, 1397, 1442], [1395, 1444, 1396, 1444], [1394, 1444, 1396, 1445], [1397, 1441, 1398, 1442], [1397, 1442, 1398, 1443], [1396, 1443, 1398, 1445], [1367, 1420, 1431, 1444], [1369, 1420, 1431, 1444], [1396, 1442, 1398, 1442], [1396, 1442, 1398, 1442], [1396, 1438, 1396, 1442], [1396, 1438, 1397, 1442], [1394, 1442, 1396, 1442], [1394, 1442, 1396, 1442], [1396, 1442, 1396, 1446], [1396, 1442, 1396, 1446]], [[25, 32, 16, 14], [32, 33, 23, 33], [33, 30, 23, 16], [39, 20, 29, 20], [33, 33, 23, 33], [25, 43, 16, 43], [43, 3, 33, 3], [35, 18, 25, 18], [19, 43, 9, 43], [20, 27, 10, 19], [21, 19, 11, 19], [27, 32, 18, 14], [27, 32, 18, 14], [22, 43, 12, 43], [22, 43, 12, 43], [14, 34, 4, 34], [12, 34, 3, 34], [31, 43, 22, 43], [31, 43, 22, 43], [25, 24, 16, 22], [22, 24, 12, 22]], \"120.32064525096493,30.312386287006067\"]"}, {"text": "5号线浙大紫金港地铁站B2口", "gis": "[[23257, 23780, 23781, 23782, 24728, 24729, 76008, 78397, 78398, 78801, 78802], [3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1238, 1427, 1254, 1444], [1244, 1427, 1244, 1427], [1243, 1426, 1245, 1427], [1244, 1427, 1245, 1428], [1242, 1425, 1244, 1427], [1244, 1425, 1246, 1426], [1208, 1425, 1261, 1488], [1242, 1426, 1249, 1427], [1242, 1426, 1249, 1427], [1176, 1392, 1256, 1433], [1176, 1393, 1256, 1433]], [[26, 23, 17, 23], [28, 23, 19, 23], [28, 24, 19, 22], [23, 3, 13, 3], [32, 31, 23, 15], [23, 34, 13, 34], [29, 23, 20, 23], [25, 26, 16, 20], [25, 28, 16, 18], [31, 31, 22, 15], [31, 31, 22, 15]], \"120.07198641101222,30.29618916994004\"]"}, {"text": "5号线万安桥地铁站B1口", "gis": "[[59073, 58801, 58802, 58804, 58806, 58807, 58808, 59062, 59064, 59066, 59067, 59068, 59069, 59074, 59075, 59105, 59106, 59109, 59110, 59111, 59112, 59113, 59114, 59115, 59116, 59117, 59118, 59119, 59120, 59126, 59127, 59128, 59129, 59130, 59131, 59132, 59136, 59137, 59138, 59139, 59140, 59141, 59142, 59143, 59144, 59145, 59146, 59147, 59148, 59149, 59150, 59151, 59152, 59153, 59154, 60199, 60200, 60208, 77681, 77700, 77701, 77781, 77782, 77785], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1307, 1385, 1308, 1388], [1307, 1387, 1307, 1387], [1306, 1383, 1307, 1390], [1306, 1385, 1307, 1390], [1307, 1386, 1307, 1387], [1307, 1386, 1308, 1387], [1308, 1387, 1308, 1387], [1307, 1383, 1308, 1386], [1307, 1386, 1307, 1386], [1308, 1385, 1308, 1386], [1308, 1385, 1308, 1385], [1308, 1386, 1308, 1386], [1307, 1385, 1307, 1386], [1308, 1386, 1308, 1386], [1308, 1386, 1308, 1386], [1308, 1385, 1309, 1386], [1308, 1386, 1308, 1386], [1308, 1386, 1308, 1386], [1308, 1385, 1309, 1387], [1308, 1386, 1308, 1386], [1309, 1386, 1309, 1386], [1309, 1386, 1309, 1386], [1308, 1386, 1308, 1387], [1308, 1386, 1308, 1387], [1307, 1387, 1308, 1387], [1307, 1387, 1307, 1387], [1307, 1388, 1308, 1388], [1308, 1387, 1308, 1387], [1308, 1387, 1308, 1388], [1307, 1388, 1307, 1388], [1307, 1388, 1308, 1388], [1307, 1387, 1308, 1389], [1307, 1388, 1308, 1388], [1308, 1388, 1308, 1388], [1308, 1387, 1308, 1389], [1308, 1388, 1308, 1389], [1308, 1387, 1308, 1387], [1308, 1386, 1308, 1387], [1308, 1386, 1309, 1387], [1308, 1387, 1308, 1387], [1308, 1387, 1308, 1387], [1308, 1387, 1308, 1387], [1308, 1387, 1308, 1387], [1309, 1386, 1309, 1387], [1309, 1387, 1309, 1387], [1309, 1387, 1309, 1387], [1309, 1387, 1309, 1387], [1308, 1387, 1308, 1387], [1308, 1387, 1308, 1388], [1308, 1387, 1308, 1388], [1308, 1388, 1308, 1388], [1309, 1387, 1309, 1387], [1309, 1387, 1309, 1388], [1308, 1388, 1308, 1388], [1308, 1388, 1309, 1388], [1306, 1386, 1307, 1386], [1306, 1386, 1307, 1387], [1306, 1387, 1307, 1387], [1307, 1383, 1308, 1390], [1307, 1378, 1308, 1396], [1307, 1378, 1308, 1396], [1307, 1383, 1307, 1386], [1307, 1383, 1308, 1386], [1307, 1383, 1308, 1389]], [[31, 29, 22, 17], [43, 17, 43, 17], [37, 28, 27, 18], [43, 26, 43, 20], [43, 30, 33, 16], [37, 29, 27, 17], [27, 39, 18, 39], [38, 38, 28, 38], [43, 43, 43, 43], [30, 43, 21, 43], [25, 43, 16, 43], [23, 43, 13, 43], [42, 39, 32, 39], [23, 41, 14, 41], [12, 43, 3, 43], [23, 43, 13, 43], [18, 43, 8, 43], [8, 43, 3, 43], [24, 32, 15, 14], [7, 43, 3, 43], [3, 43, 3, 43], [3, 43, 3, 43], [19, 43, 9, 43], [22, 30, 12, 16], [37, 18, 27, 18], [43, 3, 43, 3], [37, 3, 27, 3], [17, 3, 7, 3], [20, 19, 10, 19], [43, 3, 43, 3], [41, 3, 31, 3], [35, 20, 25, 20], [40, 3, 30, 3], [11, 3, 3, 3], [23, 22, 13, 22], [20, 4, 10, 4], [7, 42, 3, 42], [3, 38, 3, 38], [12, 37, 3, 37], [17, 25, 7, 21], [17, 25, 7, 21], [3, 29, 3, 17], [3, 29, 3, 17], [3, 43, 3, 43], [3, 33, 3, 33], [3, 33, 3, 33], [3, 22, 3, 22], [11, 17, 3, 17], [3, 3, 3, 3], [3, 3, 3, 3], [3, 3, 3, 3], [3, 7, 3, 7], [3, 3, 3, 3], [15, 3, 5, 3], [5, 6, 3, 6], [43, 43, 43, 43], [43, 43, 43, 43], [43, 39, 43, 39], [34, 28, 24, 18], [34, 28, 24, 18], [32, 28, 23, 18], [43, 37, 35, 37], [39, 37, 29, 37], [33, 29, 23, 17]], \"120.17617042944147,30.256805273907244\"]"}, {"text": "807路地铁农林大学站(B口停车场)(公交站)", "gis": "[[37092, 37059, 37180, 80994, 80995, 81095, 81096, 81125, 81126, 81127, 81128, 81129, 81133, 81134], [3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1037, 1377, 1038, 1378], [1037, 1377, 1038, 1377], [1028, 1377, 1044, 1407], [1012, 1335, 1051, 1390], [1012, 1335, 1051, 1391], [1022, 1375, 1038, 1384], [1022, 1377, 1037, 1384], [1034, 1377, 1037, 1377], [1034, 1377, 1037, 1377], [1037, 1375, 1038, 1377], [1037, 1377, 1040, 1378], [1037, 1377, 1044, 1384], [1037, 1377, 1086, 1394], [1037, 1377, 1086, 1395]], [[23, 30, 14, 16], [19, 43, 9, 43], [28, 23, 19, 23], [29, 30, 20, 16], [29, 30, 20, 16], [32, 25, 23, 21], [32, 23, 23, 23], [31, 39, 22, 39], [31, 35, 22, 35], [12, 34, 3, 34], [21, 26, 11, 20], [23, 23, 13, 23], [23, 23, 13, 23], [23, 23, 13, 23]], \"119.73272581413673,30.247325573637337\"]"}, {"text": "4号线杨家墩地铁站B口", "gis": "[[46860, 45806, 46680, 46703, 46756, 46776, 46780, 46781, 46813, 46814, 46815, 46825, 46843, 46846, 46849, 46852, 46853, 46857, 46859, 46861, 46864, 46869, 46870, 46872, 78069, 80254, 82314], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1288, 1299, 1289, 1300], [1288, 1298, 1292, 1302], [1289, 1301, 1289, 1301], [1287, 1300, 1287, 1300], [1288, 1301, 1289, 1301], [1289, 1299, 1289, 1299], [1288, 1300, 1288, 1300], [1287, 1300, 1287, 1300], [1288, 1301, 1288, 1301], [1287, 1301, 1287, 1301], [1287, 1300, 1287, 1300], [1287, 1301, 1288, 1301], [1287, 1300, 1288, 1300], [1287, 1300, 1288, 1300], [1287, 1300, 1288, 1300], [1288, 1299, 1288, 1300], [1287, 1299, 1288, 1300], [1287, 1299, 1288, 1301], [1287, 1301, 1287, 1302], [1287, 1301, 1288, 1303], [1286, 1301, 1288, 1303], [1286, 1299, 1287, 1303], [1286, 1299, 1287, 1303], [1287, 1301, 1291, 1305], [1287, 1288, 1330, 1389], [1287, 1288, 1292, 1304], [1287, 1293, 1290, 1304]], [[23, 31, 14, 15], [23, 28, 13, 18], [3, 3, 3, 3], [43, 43, 43, 43], [3, 3, 3, 3], [3, 43, 3, 43], [43, 43, 43, 43], [43, 26, 43, 20], [11, 3, 3, 3], [42, 3, 32, 3], [43, 31, 39, 15], [43, 10, 39, 10], [35, 43, 25, 43], [36, 29, 26, 17], [40, 33, 30, 33], [15, 34, 5, 34], [35, 35, 25, 35], [33, 30, 23, 16], [38, 17, 28, 17], [35, 20, 25, 20], [35, 22, 25, 22], [37, 25, 27, 21], [37, 26, 27, 20], [24, 22, 15, 22], [23, 24, 14, 22], [23, 30, 14, 16], [24, 29, 15, 17]], \"120.14364633600412,30.17066784077285\"]"}, {"text": "2号线庆春广场地铁站B3口", "gis": "[[57594, 57549, 57550, 57551, 57577, 57578, 57579, 57580, 57581, 57582, 57583, 57584, 57585, 57586, 57587, 57588, 57589, 57590, 57591, 57592, 57593, 57596, 57597, 57598, 57599, 57600, 57608, 57620, 76602, 76742, 76743, 76747, 76748, 76754, 76755, 76757, 76758], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1321, 1390, 1324, 1390], [1320, 1389, 1321, 1389], [1320, 1388, 1322, 1390], [1321, 1389, 1321, 1389], [1320, 1389, 1321, 1389], [1321, 1389, 1321, 1390], [1320, 1390, 1320, 1390], [1320, 1390, 1320, 1390], [1320, 1390, 1321, 1390], [1320, 1389, 1321, 1390], [1321, 1389, 1321, 1390], [1321, 1389, 1322, 1390], [1321, 1390, 1322, 1390], [1320, 1390, 1321, 1391], [1320, 1391, 1320, 1393], [1320, 1391, 1320, 1392], [1321, 1391, 1321, 1391], [1321, 1391, 1321, 1391], [1321, 1391, 1321, 1391], [1321, 1392, 1321, 1392], [1322, 1390, 1323, 1390], [1322, 1389, 1323, 1390], [1321, 1391, 1322, 1391], [1321, 1391, 1322, 1391], [1322, 1391, 1322, 1391], [1322, 1390, 1323, 1392], [1320, 1392, 1321, 1393], [1322, 1391, 1322, 1393], [1320, 1362, 1344, 1412], [1298, 1389, 1326, 1397], [1298, 1389, 1326, 1397], [1320, 1372, 1324, 1390], [1311, 1372, 1324, 1390], [1320, 1390, 1321, 1390], [1321, 1390, 1321, 1390], [1320, 1390, 1324, 1390], [1321, 1390, 1324, 1390]], [[23, 32, 14, 14], [43, 43, 35, 43], [28, 35, 19, 35], [36, 43, 26, 43], [43, 43, 37, 43], [43, 43, 43, 43], [43, 43, 43, 43], [43, 43, 38, 43], [43, 43, 35, 43], [34, 43, 24, 43], [38, 43, 28, 43], [25, 39, 16, 39], [28, 43, 19, 43], [35, 24, 25, 22], [43, 19, 43, 19], [43, 7, 43, 7], [41, 12, 31, 12], [20, 14, 10, 14], [28, 3, 19, 3], [31, 3, 22, 3], [14, 43, 4, 43], [12, 37, 3, 37], [17, 12, 7, 12], [14, 3, 4, 3], [3, 19, 3, 19], [16, 23, 6, 23], [31, 10, 22, 10], [15, 16, 5, 16], [23, 28, 14, 18], [31, 24, 22, 22], [31, 24, 22, 22], [26, 33, 17, 33], [30, 33, 21, 33], [40, 43, 30, 43], [43, 43, 43, 43], [26, 43, 17, 43], [24, 43, 15, 43]], \"120.19821044238523,30.26034376874256\"]"}, {"text": "入口附近中山路地铁站C口", "gis": "[[60913, 26691, 27491, 60896, 60904, 60905, 60906, 60907, 60908, 60909, 60910, 60911, 60912, 60914, 60915, 60916, 60917, 60962, 60964, 60995, 60996, 60997, 60998, 60999, 61001, 61002, 61429, 61430, 61432, 61433, 72551, 73042, 73043, 73086, 73087, 73088, 73090, 75020, 75863, 77321, 77463, 77600, 77779, 77780], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1296, 1385, 1297, 1386], [1303, 1371, 1303, 1372], [1272, 1352, 1297, 1394], [1297, 1383, 1297, 1384], [1297, 1383, 1297, 1384], [1296, 1383, 1297, 1385], [1297, 1384, 1297, 1384], [1296, 1384, 1297, 1385], [1296, 1385, 1297, 1385], [1297, 1384, 1297, 1385], [1297, 1385, 1297, 1385], [1297, 1384, 1297, 1385], [1298, 1384, 1298, 1385], [1297, 1385, 1297, 1386], [1296, 1383, 1297, 1389], [1297, 1386, 1297, 1386], [1298, 1385, 1298, 1386], [1295, 1386, 1296, 1388], [1296, 1387, 1296, 1388], [1297, 1387, 1297, 1387], [1297, 1387, 1297, 1387], [1297, 1387, 1297, 1387], [1296, 1387, 1297, 1388], [1297, 1387, 1297, 1388], [1297, 1387, 1297, 1387], [1297, 1386, 1298, 1388], [1296, 1385, 1296, 1386], [1296, 1385, 1296, 1386], [1296, 1386, 1296, 1386], [1295, 1386, 1296, 1387], [1281, 1325, 1379, 1525], [1293, 1378, 1296, 1391], [1294, 1381, 1296, 1391], [1297, 1383, 1298, 1386], [1297, 1383, 1298, 1386], [1293, 1378, 1303, 1391], [1297, 1386, 1299, 1386], [1196, 1207, 1297, 1316], [1294, 1383, 1297, 1391], [1276, 1357, 1296, 1405], [1297, 1386, 1297, 1386], [1297, 1386, 1297, 1389], [1297, 1364, 1317, 1404], [1297, 1364, 1317, 1404]], [[31, 24, 22, 22], [3, 43, 3, 43], [32, 30, 23, 16], [18, 43, 8, 43], [30, 42, 21, 42], [30, 34, 21, 34], [35, 43, 25, 43], [32, 36, 23, 36], [28, 38, 19, 38], [15, 43, 5, 43], [16, 41, 6, 41], [3, 35, 3, 35], [3, 35, 3, 35], [20, 25, 10, 21], [28, 26, 19, 20], [20, 13, 10, 13], [3, 23, 3, 23], [40, 19, 30, 19], [37, 14, 27, 14], [10, 3, 3, 3], [7, 3, 3, 3], [10, 3, 3, 3], [33, 14, 23, 14], [20, 15, 10, 15], [3, 3, 3, 3], [16, 19, 6, 19], [36, 26, 26, 20], [37, 25, 27, 21], [43, 3, 43, 3], [43, 11, 40, 11], [24, 26, 15, 20], [33, 28, 23, 18], [35, 27, 25, 19], [3, 30, 3, 16], [3, 30, 3, 16], [26, 28, 17, 18], [19, 3, 9, 3], [32, 39, 23, 39], [32, 26, 23, 20], [33, 28, 23, 18], [3, 3, 3, 3], [3, 20, 3, 20], [23, 28, 13, 18], [23, 28, 13, 18]], \"120.15800648871104,30.255307434864715\"]"}, {"text": "5号线金星地铁站B口", "gis": "[[32248, 32249, 32250, 32291, 32303, 32309, 32345, 32357, 32379, 32384, 78801, 78802, 80657, 80658, 80660], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1177, 1399, 1177, 1400], [1177, 1399, 1177, 1400], [1177, 1399, 1177, 1400], [1175, 1399, 1176, 1399], [1176, 1398, 1177, 1398], [1176, 1398, 1176, 1399], [1176, 1398, 1176, 1401], [1176, 1397, 1178, 1398], [1177, 1398, 1178, 1401], [1174, 1398, 1176, 1401], [1176, 1392, 1256, 1433], [1176, 1393, 1256, 1433], [1174, 1398, 1176, 1398], [1174, 1398, 1176, 1398], [1177, 1398, 1178, 1398]], [[3, 3, 3, 3], [5, 3, 3, 3], [18, 3, 8, 3], [43, 3, 43, 3], [32, 43, 23, 43], [43, 25, 41, 21], [37, 24, 27, 22], [26, 33, 17, 33], [23, 24, 14, 22], [34, 24, 24, 22], [23, 24, 14, 22], [23, 24, 14, 22], [36, 43, 26, 43], [35, 43, 25, 43], [18, 43, 8, 43]], \"119.96173995529142,30.26833427060563\"]"}, {"text": "人民大道505号杭州城市花园酒店(装修中)", "gis": "[[2358, 2359, 2361, 65844, 66587, 66738, 66740, 66756, 66757, 66836], [3, 3, 3, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1382, 1545, 1383, 1546], [1383, 1545, 1383, 1545], [1383, 1545, 1384, 1548], [1382, 1544, 1385, 1547], [1382, 1540, 1383, 1544], [1379, 1542, 1382, 1544], [1381, 1544, 1382, 1547], [1364, 1542, 1383, 1591], [1364, 1542, 1383, 1591], [1381, 1540, 1383, 1547]], [[26, 27, 17, 19], [15, 24, 5, 22], [21, 23, 11, 23], [23, 26, 14, 20], [25, 35, 16, 35], [33, 36, 23, 36], [35, 26, 25, 20], [32, 23, 23, 23], [32, 23, 23, 23], [29, 30, 20, 16]], \"120.29827069883349,30.414153989275412\"]"}, {"text": "2号线庆春广场地铁站B1口", "gis": "[[57594, 57550, 57551, 57577, 57578, 57580, 57581, 57582, 57583, 57584, 57585, 57586, 57589, 57590, 57591, 57592, 57593, 57595, 57596, 57597, 57598, 57599, 57600, 57620, 76602, 76742, 76743, 76747, 76748, 76754, 76755, 76757, 76758], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1321, 1390, 1324, 1390], [1320, 1388, 1322, 1390], [1321, 1389, 1321, 1389], [1320, 1389, 1321, 1389], [1321, 1389, 1321, 1390], [1320, 1390, 1320, 1390], [1320, 1390, 1321, 1390], [1320, 1389, 1321, 1390], [1321, 1389, 1321, 1390], [1321, 1389, 1322, 1390], [1321, 1390, 1322, 1390], [1320, 1390, 1321, 1391], [1321, 1391, 1321, 1391], [1321, 1391, 1321, 1391], [1321, 1391, 1321, 1391], [1321, 1392, 1321, 1392], [1322, 1390, 1323, 1390], [1322, 1389, 1323, 1390], [1322, 1389, 1323, 1390], [1321, 1391, 1322, 1391], [1321, 1391, 1322, 1391], [1322, 1391, 1322, 1391], [1322, 1390, 1323, 1392], [1322, 1391, 1322, 1393], [1320, 1362, 1344, 1412], [1298, 1389, 1326, 1397], [1298, 1389, 1326, 1397], [1320, 1372, 1324, 1390], [1311, 1372, 1324, 1390], [1320, 1390, 1321, 1390], [1321, 1390, 1321, 1390], [1320, 1390, 1324, 1390], [1321, 1390, 1324, 1390]], [[25, 31, 16, 15], [30, 35, 21, 35], [43, 43, 34, 43], [43, 43, 43, 43], [43, 43, 43, 43], [43, 43, 43, 43], [43, 43, 43, 43], [38, 43, 28, 43], [43, 43, 43, 43], [31, 39, 22, 39], [32, 43, 23, 43], [38, 23, 28, 23], [43, 10, 43, 10], [36, 12, 26, 12], [35, 3, 25, 3], [43, 3, 36, 3], [19, 43, 9, 43], [12, 43, 3, 43], [16, 36, 6, 36], [24, 9, 15, 9], [28, 3, 19, 3], [6, 17, 3, 17], [19, 23, 9, 23], [23, 16, 13, 16], [23, 28, 14, 18], [31, 24, 22, 22], [31, 24, 22, 22], [27, 33, 18, 33], [31, 33, 22, 33], [43, 43, 35, 43], [43, 43, 43, 43], [27, 43, 18, 43], [25, 43, 16, 43]], \"120.19878591191633,30.260277647368888\"]"}, {"text": "松东路125大鸡村地铁站B口", "gis": "[[184, 185, 186, 187, 66183, 66184], [3, 3, 3, 3, 4, 4], [4, 4, 4, 4, 4, 4], [[1390, 1564, 1393, 1568], [1390, 1566, 1393, 1569], [1389, 1568, 1392, 1572], [1392, 1569, 1393, 1572], [1390, 1566, 1393, 1568], [1393, 1568, 1394, 1569]], [[30, 36, 21, 36], [31, 33, 22, 33], [36, 25, 26, 21], [29, 23, 20, 23], [31, 38, 22, 38], [14, 42, 4, 42]], \"120.314992430348,30.43834047198787\"]"}, {"text": "北干山南路651号萧山博物馆(装修中)", "gis": "[[39712, 39586, 39588, 39711], [3, 3, 3, 3], [3, 4, 4, 4], [[1371, 1303, 1372, 1304], [1362, 1299, 1372, 1307], [1368, 1300, 1371, 1303], [1370, 1302, 1371, 1304]], [[27, 27, 18, 19], [31, 28, 22, 18], [34, 36, 24, 36], [38, 31, 28, 15]], \"120.28014935533284,30.173800230956186\"]"}, {"text": "2号线杭发厂地铁站B2口", "gis": "[[39556, 39555, 39558, 39559, 39560, 39568, 69654, 70256, 71335, 71336, 71338, 71339, 71340, 71946], [3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1358, 1302, 1360, 1304], [1358, 1299, 1360, 1302], [1360, 1302, 1361, 1303], [1361, 1302, 1361, 1303], [1361, 1302, 1362, 1304], [1360, 1304, 1360, 1306], [1326, 1254, 1362, 1389], [1326, 1254, 1362, 1389], [1361, 1295, 1361, 1304], [1361, 1295, 1361, 1304], [1361, 1304, 1361, 1304], [1361, 1304, 1361, 1304], [1360, 1304, 1361, 1313], [1358, 1301, 1361, 1302]], [[32, 29, 23, 17], [31, 38, 22, 38], [23, 29, 13, 17], [3, 28, 3, 18], [17, 26, 7, 20], [27, 14, 18, 14], [32, 26, 23, 20], [32, 26, 23, 20], [3, 31, 3, 15], [3, 31, 3, 15], [3, 3, 3, 3], [3, 3, 3, 3], [8, 22, 3, 22], [30, 43, 21, 43]], \"120.26174887178618,30.173278434697348\"]"}, {"text": "1号线乔司地铁站B口", "gis": "[[1433, 1423, 1425, 1426, 1427, 1428, 1429, 1430, 1431, 1432, 1434, 1435, 1436, 68956, 68965, 68972, 68981, 69009, 69010, 69011], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1371, 1479, 1372, 1480], [1370, 1478, 1371, 1479], [1371, 1478, 1371, 1479], [1370, 1479, 1371, 1479], [1370, 1479, 1370, 1480], [1370, 1478, 1372, 1481], [1370, 1480, 1371, 1480], [1370, 1480, 1370, 1480], [1371, 1480, 1371, 1480], [1372, 1479, 1372, 1480], [1372, 1479, 1372, 1480], [1370, 1480, 1371, 1481], [1371, 1480, 1371, 1481], [1372, 1473, 1373, 1479], [1372, 1472, 1373, 1482], [1372, 1481, 1374, 1489], [1372, 1472, 1373, 1489], [1366, 1456, 1373, 1479], [1372, 1456, 1379, 1503], [1372, 1456, 1379, 1503]], [[23, 28, 14, 18], [43, 43, 42, 43], [35, 41, 25, 41], [43, 43, 38, 43], [43, 40, 43, 40], [31, 30, 22, 16], [43, 29, 43, 17], [43, 15, 43, 15], [33, 21, 23, 21], [3, 28, 3, 18], [3, 28, 3, 18], [43, 7, 39, 7], [34, 3, 24, 3], [21, 34, 11, 34], [21, 31, 11, 15], [22, 22, 12, 22], [23, 27, 13, 19], [31, 33, 22, 33], [23, 28, 13, 18], [23, 28, 13, 18]], \"120.2803508093518,30.349314258748148\"]"}]} ``` 我们通过这个例子再次理解一下gis信息的表达。在这个例子中，query为"龙轩大厦(装修中)附近地铁站台B2口"，用户所在位置是"119.7474200064974,30.428317971898405"，这个位置附近在OpenStreetMap没有查询到相关几何信息。该query对应的正确POI是"6号线科海路地铁站B2口"，这个POI位于"120.08195269974871,30.131676658171365"，这个位置附近在OpenStreetMap中有以下几何体[46889, 46998, 46999, 47001, 47058, 47060, 47061, 47062, 47065, 80465, 80466, 80476, 80477, 80482, 80483]。这些几何体分别是（3表示AOI，4表示road）[3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4]，这个位置在这些几何体的附近，没有一个几何体包含该位置，因此经纬度与几何体的关系都是4，接下来的两个list分别是上面提到这些几何体的左下角与右上角位置（4位数字，离散化）以及经纬度离几何体左下角和右上角的占比（4位数字，离散化）。 训练与开发集加载方式为： ```python from modelscope.msdatasets import MsDataset # Load the afqmc dataset train_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoTES-rerank', split='train') eval_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoTES-rerank', split='validation') ``` 测试集为需要选手做预测的文件，每一行中的query为用户查询query，query_gis为用户位置信息，idx为数据编号，passages为需要进行排序的POI列表，美格尔POI包括其地址文本描述与gis信息。样例如下： ``` {"query": "丽华公寓(通惠中路)向北检验检疫科学研究所", "query_gis": "[[], [], [], [], [], \"120.59443087451544,30.315515932852602\"]", "idx": "0", "passages": [{"text": "大研街道171-6庄河检验检疫局", "gis": "[[36709], [3], [3], [[1085, 1393, 1092, 1403]], [[30, 26, 21, 20]], \"119.82005106965099,30.266879783614826\"]"}, {"text": "浙江仙寓镇71号出入境检验检疫局", "gis": "[[85415], [4], [4], [[952, 965, 954, 968]], [[30, 23, 21, 23]], \"119.59671931028191,29.837907453862414\"]"}, {"text": "学林街628号杭州电子科技大学研究生公寓38幢", "gis": "[[3709, 3710, 3618, 3629, 3654, 3533, 3664, 3668, 3670, 3671, 3672, 3673, 3674, 3675, 3683, 1179, 3690, 3694, 3695, 3696, 3697, 3706, 3707, 3711, 3712, 3714, 3715, 3716, 3717, 67708, 68098, 68100, 68101, 68102, 68103, 68110], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4], [3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1420, 1449, 1421, 1450], [1404, 1449, 1409, 1451], [1419, 1444, 1421, 1448], [1420, 1444, 1421, 1449], [1416, 1449, 1420, 1450], [1410, 1443, 1416, 1449], [1420, 1448, 1421, 1448], [1419, 1449, 1420, 1449], [1419, 1450, 1420, 1450], [1420, 1449, 1420, 1449], [1420, 1449, 1420, 1449], [1420, 1449, 1420, 1449], [1420, 1450, 1420, 1450], [1420, 1450, 1420, 1450], [1416, 1450, 1421, 1451], [1416, 1450, 1421, 1451], [1419, 1450, 1420, 1451], [1420, 1450, 1420, 1450], [1420, 1451, 1420, 1451], [1420, 1451, 1420, 1451], [1420, 1451, 1421, 1451], [1421, 1449, 1421, 1449], [1421, 1449, 1421, 1449], [1421, 1450, 1421, 1450], [1421, 1450, 1421, 1450], [1420, 1451, 1421, 1451], [1421, 1451, 1421, 1451], [1421, 1451, 1421, 1451], [1421, 1449, 1422, 1451], [1420, 1444, 1421, 1449], [1416, 1449, 1421, 1449], [1421, 1449, 1421, 1449], [1421, 1449, 1422, 1449], [1421, 1449, 1421, 1449], [1421, 1449, 1421, 1449], [1421, 1449, 1422, 1451]], [[28, 29, 19, 17], [43, 26, 43, 20], [32, 35, 23, 35], [25, 35, 16, 35], [34, 30, 24, 16], [41, 35, 31, 35], [31, 43, 22, 43], [42, 43, 32, 43], [43, 27, 43, 19], [43, 43, 40, 43], [39, 43, 29, 43], [43, 43, 38, 43], [43, 27, 41, 19], [37, 32, 27, 14], [31, 22, 22, 22], [31, 22, 22, 22], [43, 12, 40, 12], [43, 3, 35, 3], [43, 3, 35, 3], [43, 3, 33, 3], [31, 3, 22, 3], [25, 43, 16, 43], [24, 43, 15, 43], [25, 17, 16, 17], [3, 25, 3, 21], [30, 3, 21, 3], [19, 3, 9, 3], [19, 3, 9, 3], [12, 25, 3, 21], [27, 35, 18, 35], [32, 43, 23, 43], [3, 43, 3, 43], [12, 43, 3, 43], [3, 37, 3, 37], [3, 43, 3, 43], [5, 24, 3, 22]], \"120.36090817968064,30.31923702542189\"]"}, {"text": "浙江省防疫站那边金华市出入境检验检疫局", "gis": "[[13243, 13136, 13238, 13240, 13241, 13244, 13245, 13289, 73117, 73630, 74505, 75020, 75644, 75646, 75656, 75657, 75672, 75680, 75684, 75685, 75686, 76008], [3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1246, 1472, 1248, 1474], [1247, 1473, 1255, 1480], [1247, 1471, 1247, 1472], [1248, 1471, 1248, 1472], [1247, 1471, 1249, 1473], [1247, 1473, 1248, 1474], [1248, 1474, 1248, 1474], [1248, 1472, 1252, 1475], [1003, 1291, 1177, 1542], [1179, 1513, 1214, 1565], [1238, 1427, 1339, 1490], [1196, 1207, 1297, 1316], [1245, 1474, 1247, 1478], [1245, 1474, 1247, 1478], [1245, 1468, 1248, 1472], [1245, 1468, 1248, 1472], [1244, 1472, 1248, 1479], [1243, 1472, 1249, 1482], [1236, 1464, 1249, 1488], [1223, 1436, 1257, 1492], [1223, 1436, 1257, 1492], [1208, 1425, 1261, 1488]], [[26, 28, 17, 18], [23, 23, 14, 23], [28, 43, 19, 43], [16, 43, 6, 43], [23, 32, 13, 14], [16, 20, 6, 20], [3, 4, 3, 4], [21, 24, 11, 22], [37, 30, 27, 16], [42, 16, 32, 16], [23, 30, 14, 16], [28, 43, 19, 43], [32, 20, 23, 20], [31, 20, 22, 20], [29, 34, 20, 34], [29, 34, 20, 34], [29, 23, 20, 23], [30, 24, 21, 22], [31, 26, 22, 20], [30, 29, 21, 17], [30, 29, 21, 17], [30, 30, 21, 16]], \"120.07689298447707,30.342193021569845\"]"}, {"text": "西溪路681号浙江公路技师学院试验检测中心", "gis": "[[29077, 29078, 78587, 78600], [3, 3, 4, 4], [3, 4, 4, 4], [[1249, 1386, 1250, 1388], [1250, 1386, 1250, 1387], [1235, 1376, 1254, 1390], [1248, 1388, 1254, 1391]], [[25, 29, 16, 17], [17, 35, 7, 35], [30, 30, 21, 16], [25, 22, 16, 22]], \"120.08065469885283,30.25721630193966\"]"}, {"text": "通惠北路160号检验检疫局", "gis": "[[], [], [], [], [], \"120.16717358492203,30.1737892668089\"]"}, {"text": "科研路190号河南出入境检验检疫局", "gis": "[[48], [3], [4], [[1366, 1630, 1366, 1630]], [[3, 38, 3, 38]], \"120.27127943052862,30.4986506684968\"]"}, {"text": "丽华北路26号检验科", "gis": "[[40243, 40242, 71478, 72070], [3, 3, 4, 4], [3, 4, 4, 4], [[1366, 1259, 1369, 1263], [1366, 1259, 1369, 1262], [1369, 1256, 1369, 1267], [1348, 1175, 1404, 1266]], [[32, 26, 23, 20], [34, 28, 24, 18], [22, 27, 12, 19], [26, 32, 17, 14]], \"120.27637123312677,30.1311598709432\"]"}, {"text": "科研街42号附18出入境检验检疫局", "gis": "[[13294, 13295, 13159, 13292, 13293, 13296, 13299, 75417, 75419], [3, 3, 3, 3, 3, 3, 3, 4, 4], [3, 3, 4, 4, 4, 4, 4, 4, 4], [[1254, 1473, 1256, 1475], [1253, 1473, 1256, 1476], [1254, 1474, 1255, 1476], [1255, 1472, 1256, 1473], [1255, 1472, 1256, 1473], [1255, 1474, 1256, 1475], [1256, 1472, 1257, 1473], [1252, 1471, 1258, 1483], [1252, 1471, 1258, 1483]], [[28, 29, 19, 17], [29, 27, 20, 19], [32, 22, 23, 22], [21, 41, 11, 41], [22, 38, 12, 38], [18, 26, 8, 20], [17, 38, 7, 38], [28, 25, 19, 21], [28, 25, 19, 21]], \"120.09002663493416,30.34361095782949\"]"}, {"text": "科学路1号附4南通出入境检验检疫局", "gis": "[[9641, 10213, 9417, 9420, 9434, 9435, 9436, 9437, 9442, 9464, 9628, 9629, 9630, 9631, 9632, 9638, 9639, 9640, 9642, 9643, 9644, 9645, 9646, 9647, 10154, 10201, 10202, 10203, 10204, 10205, 10206, 10208, 10210, 10211, 10212, 10214, 10215, 10220, 72594, 72788, 74067, 74312], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4], [3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1312, 1459, 1315, 1462], [1313, 1459, 1313, 1461], [1311, 1460, 1313, 1462], [1313, 1461, 1313, 1462], [1313, 1461, 1314, 1461], [1313, 1461, 1313, 1462], [1313, 1461, 1313, 1462], [1313, 1462, 1313, 1462], [1313, 1461, 1314, 1463], [1314, 1461, 1314, 1462], [1313, 1458, 1314, 1459], [1313, 1459, 1314, 1459], [1314, 1459, 1314, 1459], [1313, 1459, 1315, 1459], [1314, 1459, 1314, 1460], [1314, 1459, 1314, 1460], [1314, 1460, 1314, 1460], [1313, 1460, 1314, 1460], [1313, 1460, 1314, 1461], [1314, 1461, 1314, 1461], [1314, 1460, 1314, 1460], [1314, 1460, 1314, 1460], [1314, 1461, 1314, 1461], [1314, 1461, 1314, 1461], [1312, 1457, 1315, 1459], [1312, 1459, 1313, 1459], [1313, 1459, 1313, 1459], [1313, 1458, 1313, 1459], [1313, 1459, 1313, 1459], [1312, 1459, 1313, 1459], [1312, 1459, 1313, 1460], [1312, 1459, 1312, 1460], [1312, 1461, 1312, 1461], [1312, 1460, 1313, 1460], [1312, 1460, 1313, 1460], [1312, 1460, 1313, 1461], [1313, 1461, 1313, 1461], [1312, 1461, 1312, 1461], [1296, 1451, 1358, 1523], [1296, 1451, 1358, 1522], [1289, 1445, 1320, 1504], [1290, 1453, 1319, 1498]], [[27, 26, 18, 20], [28, 28, 19, 18], [37, 21, 27, 21], [34, 3, 24, 3], [12, 3, 3, 3], [22, 3, 12, 3], [13, 3, 3, 3], [3, 3, 3, 3], [26, 13, 17, 13], [17, 3, 7, 3], [19, 43, 9, 43], [20, 43, 10, 43], [5, 43, 3, 43], [23, 43, 13, 43], [14, 43, 4, 43], [3, 43, 3, 43], [15, 39, 5, 39], [15, 27, 5, 19], [16, 16, 6, 16], [10, 3, 3, 3], [3, 38, 3, 38], [3, 27, 3, 19], [3, 3, 3, 3], [3, 6, 3, 6], [26, 37, 17, 37], [43, 43, 36, 43], [43, 43, 36, 43], [30, 43, 21, 43], [33, 43, 23, 43], [39, 43, 29, 43], [40, 43, 30, 43], [43, 30, 43, 16], [43, 3, 39, 3], [40, 41, 30, 41], [41, 28, 31, 18], [41, 19, 31, 19], [32, 3, 23, 3], [43, 3, 33, 3], [25, 24, 16, 22], [25, 24, 16, 22], [30, 25, 21, 21], [31, 24, 22, 22]], \"120.18493879769271,30.329512189620193\"]"}, {"text": "科华北路71号中国检验检疫科学研究院", "gis": "[[4861, 4868, 67512], [3, 3, 4], [3, 4, 4], [[1381, 1426, 1384, 1429], [1383, 1429, 1385, 1430], [1381, 1425, 1384, 1428]], [[28, 28, 19, 18], [18, 14, 8, 14], [27, 34, 18, 34]], \"120.29912825151396,30.297699387244755\"]"}, {"text": "华中南路668号海润检测", "gis": "[[8906, 8907, 8908, 8909, 8910, 8911, 8912, 68747, 72392, 72551, 73897, 74409, 74505, 74713, 74723, 74724, 74725, 74726, 74727, 74728, 74729, 74730, 74731, 74734, 74735], [3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1318, 1457, 1321, 1457], [1320, 1457, 1320, 1457], [1321, 1456, 1321, 1457], [1320, 1457, 1321, 1457], [1320, 1457, 1321, 1458], [1321, 1457, 1321, 1457], [1321, 1457, 1321, 1458], [1312, 1419, 1358, 1476], [1302, 1438, 1327, 1484], [1281, 1325, 1379, 1525], [1249, 1390, 1330, 1534], [1312, 1438, 1326, 1465], [1238, 1427, 1339, 1490], [1238, 1427, 1339, 1490], [1319, 1457, 1320, 1457], [1319, 1457, 1320, 1457], [1320, 1453, 1320, 1457], [1320, 1453, 1321, 1459], [1320, 1457, 1320, 1457], [1320, 1457, 1320, 1457], [1316, 1457, 1326, 1457], [1320, 1457, 1321, 1459], [1316, 1457, 1326, 1457], [1320, 1445, 1328, 1466], [1320, 1445, 1328, 1466]], [[31, 13, 22, 13], [43, 16, 34, 16], [33, 21, 23, 21], [28, 3, 19, 3], [37, 12, 27, 12], [23, 3, 14, 3], [27, 19, 18, 19], [24, 29, 15, 17], [30, 26, 21, 20], [27, 29, 18, 17], [31, 27, 22, 19], [28, 29, 19, 17], [31, 27, 22, 19], [31, 27, 22, 19], [35, 3, 25, 3], [35, 3, 25, 3], [41, 31, 31, 15], [36, 28, 26, 18], [43, 3, 43, 3], [43, 3, 43, 3], [27, 20, 18, 20], [36, 21, 26, 21], [27, 17, 18, 17], [24, 28, 15, 18], [24, 28, 15, 18]], \"120.19737288311275,30.3258363287553\"]"}, {"text": "共和南路56号附近富阳区人民检察院新登检察室", "gis": "[[85022, 85029], [4, 4], [4, 4], [[1036, 1098, 1038, 1108], [1036, 1098, 1038, 1108]], [[25, 25, 16, 21], [26, 25, 17, 21]], \"119.73244418767773,29.97238072399028\"]"}, {"text": "石祥路31新天地7幢杭州致远医学检验所", "gis": "[[10007, 10198, 9417, 9641, 10094, 10095, 10121, 10174, 10175, 10176, 10177, 10178, 10179, 10180, 10181, 10182, 10184, 10185, 10186, 10187, 10188, 10189, 10190, 10191, 10192, 10193, 10194, 10195, 10197, 10199, 10200, 10207, 10208, 10209, 10216, 72373, 72374, 72375, 72376, 72384, 72392, 74063, 74362, 74439, 74441, 74442], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1306, 1454, 1312, 1462], [1311, 1460, 1311, 1460], [1311, 1460, 1313, 1462], [1312, 1459, 1315, 1462], [1309, 1460, 1310, 1460], [1309, 1460, 1310, 1460], [1311, 1458, 1311, 1458], [1310, 1458, 1311, 1459], [1310, 1459, 1310, 1459], [1310, 1459, 1310, 1459], [1310, 1459, 1310, 1459], [1310, 1459, 1310, 1460], [1310, 1459, 1310, 1460], [1310, 1459, 1310, 1459], [1310, 1459, 1311, 1459], [1310, 1459, 1310, 1459], [1310, 1459, 1310, 1460], [1310, 1460, 1310, 1460], [1310, 1459, 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3], [30, 43, 21, 43], [14, 42, 4, 42], [23, 43, 14, 43], [21, 42, 11, 42], [7, 43, 3, 43], [27, 43, 18, 43]], \"120.3378324236174,30.318456952026786\"]"}, {"text": "空港大道中国检验检疫(北门)", "gis": "[[722, 37308], [3, 3], [3, 4], [[1444, 1348, 1481, 1385], [1453, 1349, 1478, 1370]], [[25, 24, 16, 22], [23, 25, 14, 21]], \"120.41400680184667,30.223792703297068\"]"}, {"text": "铁科研专用线中国检验检疫科学研究院", "gis": "[[37499, 40344, 70246], [3, 3, 4], [4, 4, 4], [[1391, 1220, 1392, 1222], [1380, 1206, 1416, 1259], [1386, 1216, 1392, 1226]], [[41, 34, 31, 34], [26, 25, 17, 21], [33, 29, 23, 17]], \"120.31424608480508,30.092855883772298\"]"}, {"text": "大园路1623号香港大学浙江科学技术研究院", "gis": "[[36495, 35935, 36499, 79187, 79194], [3, 3, 3, 4, 4], [3, 4, 4, 4, 4], [[1087, 1413, 1088, 1415], [1057, 1396, 1131, 1474], [1087, 1409, 1089, 1413], [1087, 1393, 1093, 1416], [1087, 1413, 1089, 1416]], [[28, 29, 19, 17], [27, 25, 18, 21], [24, 35, 15, 35], [24, 32, 15, 14], [26, 26, 17, 20]], \"119.81615603673362,30.283741895920368\"]"}, {"text": "海通街580号浙江省计量科学研究院北区(东门)", "gis": "[[4655, 4660, 4661, 4666], [3, 3, 3, 3], [4, 4, 4, 4], [[1393, 1433, 1394, 1437], [1395, 1434, 1396, 1435], [1395, 1435, 1395, 1435], [1394, 1435, 1396, 1438]], [[32, 31, 23, 15], [22, 38, 12, 38], [8, 43, 3, 43], [23, 25, 13, 21]], \"120.31792698593385,30.30560930208081\"]"}, {"text": "五常街道向往街199号1幢2单元3-4层杭州度安医学检>验实验室有限公司", "gis": "[[31229, 31239, 29994, 30828, 30830, 31227, 31228, 31230, 31231, 31232, 31233, 31234, 31235, 31236, 31237, 31238, 31240, 31241, 31242, 31246, 31247, 32580, 32584, 32615, 32649], [3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3], [3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4], [[1208, 1417, 1210, 1420], [1210, 1419, 1210, 1420], [1208, 1420, 1210, 1422], [1210, 1421, 1210, 1421], [1210, 1420, 1211, 1422], [1209, 1418, 1209, 1418], [1209, 1418, 1209, 1419], [1209, 1419, 1209, 1419], [1209, 1419, 1209, 1419], [1209, 1418, 1210, 1418], [1209, 1419, 1210, 1419], [1209, 1419, 1209, 1419], [1209, 1419, 1209, 1419], [1209, 1419, 1209, 1419], [1209, 1419, 1209, 1420], [1210, 1419, 1210, 1419], [1210, 1418, 1210, 1419], [1210, 1419, 1210, 1419], [1211, 1419, 1211, 1419], [1210, 1419, 1211, 1421], [1209, 1417, 1214, 1421], [1209, 1419, 1209, 1419], [1209, 1418, 1209, 1419], [1209, 1418, 1209, 1418], [1209, 1418, 1209, 1419]], [[29, 30, 20, 16], [26, 27, 17, 19], [32, 23, 23, 23], [21, 11, 11, 11], [23, 18, 13, 18], [43, 43, 40, 43], [42, 43, 32, 43], [43, 43, 43, 43], [39, 43, 29, 43], [33, 43, 23, 43], [36, 43, 26, 43], [43, 43, 38, 43], [42, 43, 32, 43], [43, 32, 43, 14], [43, 27, 37, 19], [23, 43, 14, 43], [21, 43, 11, 43], [10, 43, 3, 43], [3, 43, 3, 43], [20, 27, 10, 19], [24, 29, 15, 17], [43, 43, 43, 43], [43, 43, 43, 43], [43, 43, 41, 43], [43, 34, 43, 34]], \"120.01575865720118,30.289186149333478\"]"}, {"text": "杭州中新街(惠港二路)76饶平县检验检疫局", "gis": "[[], [], [], [], [], \"120.20509044775532,30.076259797983873\"]"}]} ``` 选手需要对测试集做预测，输出测试结果。测试结果每行为一条预测，包括query、query_gis、idx、positive_passages信息。样例如下： ``` {"query": "丽华公寓(通惠中路)向北检验检疫科学研究所", "query_gis": "[[], [], [], [], [], \"120.59443087451544,30.315515932852602\"]", "positive_passages": [{"text": "通惠中路118号浙江省检验检疫科学技术研究院萧山分院", "gis": "[[39707, 39709, 71583, 71584, 71631, 71632, 71643, 71644], [3, 3, 4, 4, 4, 4, 4, 4], [4, 4, 4, 4, 4, 4, 4, 4], [[1372, 1298, 1372, 1298], [1368, 1295, 1377, 1302], [1372, 1295, 1372, 1299], [1372, 1296, 1372, 1299], [1372, 1299, 1372, 1300], [1372, 1299, 1372, 1300], [1372, 1300, 1373, 1309], [1372, 1300, 1373, 1309]], [[34, 43, 24, 43], [28, 28, 19, 18], [43, 32, 35, 14], [43, 32, 34, 14], [43, 23, 41, 23], [43, 22, 43, 22], [31, 22, 22, 22], [29, 23, 20, 23]], \"120.28211529274846,30.169120927603558\"]"}]} ``` ### 地址Query成分分析 #### 任务描述 用户在地图类服务中输入地址query用以查询相关地理元素信息。地址query除了包含标准门址信息外通常还包含： - 公交、地铁站点、线路信息； - 品牌、商圈名称； - 修饰信息如：24小时，便宜的； - 语义连接词如： 助词、疑问词、连接词、转折词、需求词、附近词、量词。 相比标准门址，地址query的表达更丰富，信息量更大。本任务需要对地址query进行成分分析。 #### 任务说明 - 输入：地址query - 输出：query中包含的成分片段及类型 #### 标签说明 | Label | Name | |----------------------------------|-------------------------------------------------------------------------------------------------| | BS | 公交地铁站 | | BL | 公交地铁线路 | | RD | 道路、(高速公路、阜荣街)、隧道、桥梁、立交桥 | | Entity | POI一般名称:黄山风景区、首开广场、清华大学、(幼儿园)春蕾 | | Brand | 著名品牌:((阜荣街)外婆家) | | CategorySuffix | 类别后缀词:(阜荣街)大学（二层) | | PA | 国家 | | PB | 省 | | PC | 城市 | | PD | 区县(包括开发区) | | PE | 乡镇 | | PF | 街道 | | PG | 村庄 | | PH | 行政俗称/商圈：九亭、古荡、莘庄、望京 | | PS | 其它行政 | | UA | 门址 道路xx号/xx弄 | | UB | 门址 xx座楼/xx区 | | UC | 门址 xx号楼/xx棟/xx幢/ | | UD | 门址 附属描述(三期、北段、一段、(机场)出发、内环、外环、路口、交叉口、下道口、交汇处、高速出口) | | UE | 门址 东口、南门 | | Desc |修饰词：24小时，便宜的 | | Yewu |业务词 | | SS | 分支词：(肯德基)xx店、(xx)紫金港校区、(xx)望京支行、(xx)二厂 | | SA | 方位修饰词；南，向南，东侧、路南、桥北 | | YA | 语义连接词: 助词、疑问词、连接词、转折词、需求词、附近词、量词 | | BD | 标点符号 | | NumEng | 数字英文串(品牌、entity、明确的门址除外) | | ZZ | 未知 | #### 评测指标 我们通过将输出结果与人工标注的集合进行比较来分别计算每一种元素准确率(Precision)，召回率(Recall)和F-1分值(F-1 score)，并采用Micro-F1作为最终排名指标。 具体计算过程如下所示（以元素T为例）： 给定一条地理文本输入（n），此中有x个T元素： G_n={g_1,g_2,g_3…g_x}，系统输出标注结果中有y个T元素如下：P_n={p_1,p_2,p_3…p_y}，则实体标注的准确率定义如下： ![F1计算](fig/f1_1.png) 实体标注的召回率定义如下： ![F1计算](fig/f1_2.png) 实体标注的F1值定义如下： ![F1计算](fig/f1_2.png) 其中N为整个测试集。 #### 评测数据 本评测开放训练集数据数量49784，验证集2214数量，测试集数量50001。 数据集名称为：GeoCPA（Geographic ComPosition Analysis）。 其中训练集与验证集包含输入文本与标注结果。每一行为一条标注结果，包括tokens和ner_tags信息。tokens为输入文本字符列表，按照字粒度切分；ner_tags为标签列表，按照BIO规范标记。样例如下： ```json {"tokens": ["亳", "州", "南", "高", "速", "出", "口"], "ner_tags": ["B-PC", "I-PC", "B-SA", "B-UD", "I-UD", "I-UD", "I-UD"]} ``` 训练与开发集加载方式为： ```python from modelscope.msdatasets import MsDataset # Load the afqmc dataset train_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoCPA', split='train') eval_datasets = MsDataset.load('GeoGLUE', namespace='damo', subset_name='GeoCPA', split='validation') ``` 测试集为需要选手做预测的文件，每一行为一条需要预测的文本及其idx。样例如下： ``` {"tokens": ["伊", "宁", "的", "奶", "皮", "子", "什", "么", "地", "方", "有", "卖", "的"], "idx": "0"} ``` 选手需要对测试集做预测，输出测试结果。测试结果每行为一条预测，括idx、tokens和ner_tags信息。样例如下： ``` {"idx": "1", "tokens": ["南", "大", "街"], "ner_tags": ["B-RD", "I-RD", "I-RD"]} ``` ### WhereWhat切分 #### 任务描述 当一条地址包含多个地点描述时，通常需要对其进行切分，将原始地址切为where和what两部分。例如： 三里屯酒吧 这条描述，包含两个部分：三里屯、酒吧。用户的意图是想找三里屯附近的酒吧。因此需要将原始地址的“三里屯”识别为where部分，“酒吧”识别为what部分。 本任务目标是将输入地址做where和what的切分与识别。 #### 任务说明 - 输入：地址query - 输出：query中的where部分和what部分 #### 评测指标 我们通过将输出结果与人工标注的集合进行比较来分别计算每一种元素准确率(Precision)，召回率(Recall)和F-1分值(F-1 score)，并采用Micro-F1作为最终排名指标。 具体计算过程如下所示（以元素T为例）： 给定一条地理文本输入（n），此中有x个T元素： G_n={g_1,g_2,g_3…g_x}，系统输出标注结果中有y个T元素如下：P_n={p_1,p_2,p_3…p_y}，则实体标注的准确率定义如下： ![F1计算](fig/f1_1.png) 实体标注的召回率定义如下： ![F1计算](fig/f1_2.png) 实体标注的F1值定义如下： ![F1计算](fig/f1_2.png) 其中N为整个测试集。 #### 评测数据 本评测开放训练集数据数量9311，验证集1597数量，测试集数量50002。 数据集名称为：GeoWWC（Geographic Where What Cut）。 其中训练集与验证集包含输入文本与标注结果。每一行为一条标注结果，包括tokens和ner_tags信息。tokens为输入文本字符列表，按照字粒度切分；ner_tags为标签列表，按照BIEOS规范标记。样例如下： ```json {"tokens": ["霍", "邱", "县", "周", "集", "镇", "周", "集", "港", "中", "化", "六", "安", "油", "库"], "ner_tags": ["B-/WHERE", "I-/WHERE", "I-/WHERE", "I-/WHERE", "I-/WHERE", "I-/WHERE", "I-/WHERE", "I-/WHERE", "E-/WHERE", "B-/WHAT", "I-/WHAT", "I-/WHAT", "I-/WHAT", "I-/WHAT", "E-/WHAT"]} ``` 训练与开发集加载方式为： ```python from modelscope.msdatasets import MsDataset # Load the afqmc dataset train_datasets = MsDataset.load('GeoGLUE', subset_name='GeoWWC', split='train') eval_datasets = MsDataset.load('GeoGLUE', subset_name='GeoWWC', split='validation') ``` 测试集为需要选手做预测的文件，每一行为一条需要预测的文本及其idx。样例如下： ``` {"tokens": ["徐", "州", "到", "曹", "县"], "idx": "8"} ``` 选手需要对测试集做预测，输出测试结果。测试结果每行为一条预测，括idx、tokens和ner_tags信息。样例如下： ``` {"idx": "1", "tokens": ["龙", "江", "西", "庆", "冰", "鲜", "场"], "ner_tags": ["B-/WHERE", "E-/WHERE", "B-/WHAT", "I-/WHAT", "I-/WHAT", "I-/WHAT", "E-/WHAT"]} ``` ### 引用信息 ### License 该数据集遵循协议：[CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/?spm=5176.12282016.0.0.63b47586Niz8D0) 如果对于License您有任何疑惑或其他信息，抑或我们在无意中侵犯了您的权利，请及时联系我们。 ### 使用答疑 如果您在使用中遇到任何困难，请钉钉搜索加入答疑群，群号：26735013283