Building footprtints from 1970s Hexagon spy satellite images for four global urban growth hotspots

This dataset features building footprints derived from 1970s very-high resolution KH-9 Hexagon spy satellite imagery using a Mask R-CNN deep learning object detection approach for four sites: San Diego County (USA), Madison (USA), Harare (Zimbabwe), and Hyderabad (India). It also contains contemporary building footprint data from Microsoft’s building footprint layer (https://github.com/microsoft/GlobalMLBuildingFootprints) as a reference.

Corresponding publication

Franz Schug*; Neda K. Kasraee, Akash Anand, MacKenzy T. Growth-Price, Mihai D. Nita, Afag Rizayeva, Volker C. Radeloff. Quantifying multi-decadal urban growth using Hexagon spy satellite imagery and deep learning building detection across four global cities (in review). Landscape and Urban Planning.

Temporal extent

The data contains data representative for ca. 1975 and ca. 2020.

Data, data format, and units

KH-9 Hexagon data (“hex_images”) are provided as geotiff files with a spatial resolution of ~ 0.6 – 1 m. The coordinate reference systems (CRS) are local UTM projections (San Diego County: Zone 11N, EPSG 32611, Madison: Zone 16N, EPSG 32616, Harare: Zone 36S, EPSG 32736, Hyderabad: Zone 44N, EPSG 32644).

Microsoft building footprint data (“ms_buildings”) are provided as vector shape files. The data were clipped from Microsoft’s global building footprint layer (https://github.com/microsoft/GlobalMLBuildingFootprints) and are provided as a reference for ca. 2020. CRS correspond to the CRS of Hexagon data. The data are also provided in a rasterized version with 2-m and 300-m spatial resolution (nearest neighbor resampling).

Study site extents (“study_sites”) are provided as vector shape files.

Training chips (“training_chips”) contain image chips and labels used for training the Mask R-CNN models. The metadata format is “Mask RCNN Masks”. The data include no-feature tiles.

The models (“models”) are trained Mask R-CNN models ready to be used in ESRI ArcGIS Pro version 3.2.1. Please refer to the publication for details about model parameterization.

The detected buildings (“detected_buildings”) are provided as vector shape files and represent building footprints from ca. 1975 Hexagon imagery using the provided Mask R-CNN models. The data represent the final results, that means, after merging models with different chip sizes and post-processing (see manuscript). The data are also provided in a rasterized version with 2-m and 300-m spatial resolution (nearest neighbor resampling).

Processing environment

This research has been conducted using Python for ESRI ArcGIS Pro version 3.2.1 and the TensorFlow package. We conducted our analysis on a server with an NVIDIA A100 Tensor Core GPU (40GB, PCIe), a Dual AMD EPYC 7513 CPU with 2.6GHz and 128 threads in total, and 1 TB RAM (RDIMM, 3200MT/s Dual Rank).

Further information

For further information, please see the publication or contact Franz Schug ([e-mail]). Visit the website of SILVIS lab, University of Wisconsin-Madison (https://silvis.forest.wisc.edu/) to learn more.

Please check the corresponding github repository for additional data and code: https://github.com/franzschug/hexagon_bld_footprints

Acknowledgments

This study was supported by the NASA Land Cover and Land Use Change Program under agreement 80NSSC21K0310, the NASA IDS program under agreement 80NSSC24K0303, and the USDA McIntire Stennis Program.

本数据集包含基于20世纪70年代超高分辨率KH-9 Hexagon（锁眼-9六边形）间谍卫星影像提取的建筑足迹，针对四个研究区域：美国圣迭戈县、美国麦迪逊、津巴布韦哈拉雷以及印度海得拉巴，采用Mask R-CNN（掩码区域卷积神经网络）深度学习目标检测方法完成提取。同时还包含来自微软（Microsoft）建筑足迹图层（https://github.com/microsoft/GlobalMLBuildingFootprints）的同期建筑足迹数据作为参照。

对应发表文献
Franz Schug*; Neda K. Kasraee, Akash Anand, MacKenzy T. Growth-Price, Mihai D. Nita, Afag Rizayeva, Volker C. Radeloff. 《利用六边形间谍卫星影像与深度学习建筑检测方法量化全球四座城市的数十年城市扩张》（已投稿）. 《景观与城市规划》（Landscape and Urban Planning）.

时间范围
本数据集涵盖约1975年与约2020年两个时段的代表性数据。

数据、数据格式与单位
KH-9 Hexagon影像数据（命名为"hex_images"）以GeoTIFF（地理标记图像文件格式）文件格式提供，空间分辨率约为0.6–1米。坐标参考系统（CRS）采用通用横轴墨卡托（UTM）局部投影：圣迭戈县为11N带，EPSG:32611；麦迪逊为16N带，EPSG:32616；哈拉雷为36S带，EPSG:32736；海得拉巴为44N带，EPSG:32644。
微软建筑足迹数据（命名为"ms_buildings"）以矢量形状文件格式提供，该数据从微软（Microsoft）全球建筑足迹图层（https://github.com/microsoft/GlobalMLBuildingFootprints）裁剪得到，作为约2020年的参照数据。其坐标参考系统与KH-9 Hexagon影像数据一致。此外还提供了空间分辨率为2米与300米的栅格化版本（采用最邻近邻域重采样）。
研究区域范围数据（命名为"study_sites"）以矢量形状文件格式提供。
训练样本块（training_chips）包含用于训练Mask R-CNN模型的图像块与标注标签，元数据格式为"Mask RCNN Masks"，数据包含无特征图块。
预训练模型（命名为"models"）为可直接在ESRI ArcGIS Pro 3.2.1版本中使用的Mask R-CNN模型，模型参数化细节请参阅发表文献。
检测得到的建筑足迹（命名为"detected_buildings"）以矢量形状文件格式提供，对应利用本数据集提供的Mask R-CNN模型从约1975年的KH-9 Hexagon影像中提取的建筑足迹。该数据为最终处理结果，即经过不同样本块尺寸模型融合与后处理后的结果（详见论文）。同时还提供了空间分辨率为2米与300米的栅格化版本（采用最邻近邻域重采样）。

计算环境
本研究使用Python针对ESRI ArcGIS Pro 3.2.1版本以及TensorFlow库开展。分析工作在搭载NVIDIA A100 Tensor Core GPU（40GB，PCIe接口）、双路AMD EPYC 7513 CPU（2.6GHz，总计128线程）以及1TB RDIMM内存（3200MT/s双列）的服务器上完成。

补充信息
如需更多信息，请参阅发表文献或联系Franz Schug（邮箱：[e-mail]）。可访问威斯康星大学麦迪逊分校SILVIS实验室官网（https://silvis.forest.wisc.edu/）了解更多详情。请访问对应GitHub仓库获取额外数据与代码：https://github.com/franzschug/hexagon_bld_footprints

致谢
本研究得到美国国家航空航天局（NASA）土地覆盖与土地利用变化项目（协议号80NSSC21K0310）、NASA IDS项目（协议号80NSSC24K0303）以及美国农业部McIntire Stennis项目的资助。