Analyzing Satellite-Derived 3D Building Inventories and Quantifying Urban Growth towards Active Faults: A Case Study of Bishkek, Kyrgyzstan

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Datasets supporting the publication:
Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults:
a case study of Bishkek, Kyrgyzstan.
https://doi.org/10.3390/rs14225790

-Please refer to the publication for details on the production of each dataset.
-Datasets are ordered following the publication figures.
-Please cite the publication and this dataset repository when using the data.
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Structure:
File ID
-[fields:] description
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KH9_1979_builtup.shp
-KH9 1979 built-up area classification

S2_2021_builtup.tif
-Sentinel-2 2021 built-up area classification.

S2_2021_corine_land_cover_class.tif
-Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.

S2_KH9_DN_change_aggregated.shp
-Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.

building_characteristics.shp
-build_count: building count in 500 m square grid cell.
-mean_area: mean building size (m^2) in  500 m square grid cell.
-median_area: median building size(m^2) in  500 m square grid cell.
-cell_coverage: %building coverage of 500 m square grid cell.

pleiades_buildings_all.shp
-All building detections from Pleiades data. Confidence values are output from the deep learning model.

pleiades_buildings_heights.shp
-Building detections from the Pleiades data that were allocated heights (m).
-Zmean, Zmedian,... refer to heights (m)

wv2_buildings_all.shp
-All building detections from WorldView-2 data. Confidence values are output from the deep learning model.

wv2_buildings_heights.shp
-Building detections from the WorldView-2 data that were allocated heights (m).
-Zmean, Zmedian,... refer to heights (m)

trained_rcnn.zip
-ArcGIS Pro deep learning model (DLPK) used to extract building footprints.

本数据集支撑以下发表论文：《分析卫星获取的三维建筑清单并量化面向活动断层的城市扩张——以吉尔吉斯斯坦比什凯克为例》，论文DOI：10.3390/rs14225790

- 各数据集的生产制作细节请参阅上述发表论文。
- 数据集排序与论文附图保持一致。
- 若使用本数据集，请同时引用该发表论文与本数据集仓库。

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数据集结构：
文件标识
-[字段:] 说明
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KH9_1979_builtup.shp
-[字段:] 1979年KH9影像建成区分类成果

S2_2021_builtup.tif
-[字段:] 2021年Sentinel-2（哨兵二号）影像建成区分类结果

S2_2021_corine_land_cover_class.tif
-[字段:] 采用Corine 2018土地覆盖分类体系的2021年Sentinel-2影像土地覆盖分类结果

S2_KH9_DN_change_aggregated.shp
-[字段:] 针对建成占比≥50%的区域，将DN值变化比例聚合至1 km²网格后的成果

building_characteristics.shp
-[字段:] build_count：500米正方形网格单元内的建筑总数量；
mean_area：500米正方形网格单元内的平均建筑面积（单位：平方米）；
median_area：500米正方形网格单元内的建筑面积中位数（单位：平方米）；
cell_coverage：500米正方形网格单元的建筑覆盖率（百分比）

pleiades_buildings_all.shp
-[字段:] 基于Pleiades（普莱阿德斯）影像的全部建筑检测结果，置信度值由深度学习模型输出

pleiades_buildings_heights.shp
-[字段:] 已赋予高度值（单位：米）的Pleiades影像建筑检测结果；
Zmean、Zmedian等指标均指代建筑高度（单位：米）

wv2_buildings_all.shp
-[字段:] 基于WorldView-2（世界视图二号）影像的全部建筑检测结果，置信度值由深度学习模型输出

wv2_buildings_heights.shp
-[字段:] 已赋予高度值（单位：米）的WorldView-2影像建筑检测结果；
Zmean、Zmedian等指标均指代建筑高度（单位：米）

trained_rcnn.zip
-[字段:] 用于提取建筑footprint（建筑轮廓）的ArcGIS Pro深度学习模型包（DLPK）