Land Use Land Cover (LULC) analysis on satellite images using deep
learning-based methods is significantly helpful in understanding the geography,
socio-economic conditions, poverty levels, and urban sprawl in developing
countries. Recent works involve segmentation with LULC classes such as
farmland, built-up areas, forests, meadows, water bodies, etc. Training deep
learning methods on satellite images requires large sets of images annotated
with LULC classes. However, annotated data for developing countries are scarce
due to a lack of funding, absence of dedicated residential/industrial/economic
zones, a large population, and diverse building materials. BD-SAT provides a
high-resolution dataset that includes pixel-by-pixel LULC annotations for Dhaka
metropolitan city and surrounding rural/urban areas. Using a strict and
standardized procedure, the ground truth is created using Bing satellite
imagery with a ground spatial distance of 2.22 meters per pixel. A three-stage,
well-defined annotation process has been followed with support from GIS experts
to ensure the reliability of the annotations. We performed several experiments
to establish benchmark results. The results show that the annotated BD-SAT is
sufficient to train large deep learning models with adequate accuracy for five
major LULC classes: forest, farmland, built-up areas, water bodies, and
meadows.

利用基于深度学习方法的卫星图像地表覆盖（LULC）分析对于了解发展中国家的地理、社会经济条件、贫困水平和城市扩张具有重要意义。在缺乏资金、缺乏专门的居住区 / 工业区 / 经济区、庞大的人口和多样化建筑材料的情况下，BD-SAT 提供了一种高分辨率数据集，包括达卡大都会城市及周边农村 / 城市地区的像素级 LULC 注释。通过严格和标准化的过程，使用 Bing 卫星图像创建了地面真实性数据，每个像素的地面空间距离为 2.22 米。我们进行了几个实验以建立基准结果，结果表明 BD-SAT 提供的注释足以训练具备足够准确性的大型深度学习模型，其中包括五个主要的 LULC 类别：森林、农田、建筑区域、水体和草地。