Buildings and roads detected from 0.5 meter resolution Maxar satellite imagery, Alaskan communities, 2018-2023

We created HABITAT (High-resolution Arctic Built Infrastructure and Terrain Analysis Tool), a deep learning-based, high-performance computing-enabled mapping pipeline to automatically detect buildings and roads from high-resolution Maxar satellite imagery in Arctic communities. The code is made available at https://github.com/PermafrostDiscoveryGateway/HABITAT.
The pipeline is based on a ResNet50-UNet++ semantic segmentation architecture trained on a training dataset comprised of building and road footprint polygons manually digitized from Maxar satellite imagery across the circumpolar Arctic (including Alaska, Russia, and Canada).
From imagery of 285 Alaskan communities acquired between 2018-2023, we detected approximately 250,000 buildings and storage tanks (comprising a 41.76 million square meter footprint) and 15 million meters of road. Building (including storage tanks) footprint polygons and road centerlines were strictly mapped within the boundaries of Alaskan communities (both incorporated places and census designated places). After the deep learning model detected building and road footprints, post-processing was performed to smooth out building footprints, extract centerlines from road footprints, and remove falsely-detected infrastructure. In particular, a buffer is created around developed land cover identified by the 2016 Alaska National Land Cover Database map, and model predictions that fall outside of the buffer are assumed to be confused with non-infrastructure land cover.

我们研发了HABITAT（高分辨率北极建筑基础设施与地形分析工具，High-resolution Arctic Built Infrastructure and Terrain Analysis Tool），这是一款基于深度学习、搭载高性能计算能力的制图流水线，可从北极社区的高分辨率Maxar卫星影像中自动识别建筑与道路。该工具的代码已开源，访问地址为https://github.com/PermafrostDiscoveryGateway/HABITAT。
该流水线基于ResNet50-UNet++语义分割（semantic segmentation）架构构建，其训练数据集由环北极地区（涵盖阿拉斯加、俄罗斯与加拿大）的Maxar卫星影像中手动数字化提取的建筑与道路占地轮廓多边形构成。
我们利用2018年至2023年间获取的285个阿拉斯加社区的卫星影像，共识别出约25万座建筑及存储罐（总占地达4176万平方米），以及总长1500万米的道路。建筑（含存储罐）占地轮廓多边形及道路中心线均严格限定在阿拉斯加社区的行政边界范围内，包括建制社区及人口普查指定社区。
在深度学习模型识别出建筑与道路占地轮廓后，我们开展了后处理工作：对建筑占地轮廓进行平滑优化，从道路占地轮廓中提取中心线，并移除误识别的基础设施。具体而言，我们基于2016年《阿拉斯加国家土地覆盖数据库》图件识别的已开发土地覆盖范围创建缓冲区，模型预测结果若落在缓冲区外，则判定为与非基础设施土地覆盖混淆的误识别结果。