BD-SAT: High-resolution Land Use Land Cover Dataset & Benchmark Results for Developing Division: Dhaka, BD

TL;DR

This paper introduces BD-SAT, a high-resolution satellite image dataset with detailed land use annotations for Dhaka, enabling improved deep learning-based land cover classification in developing regions.

Contribution

The creation of BD-SAT, a high-resolution, pixel-level annotated satellite dataset for Dhaka, addressing data scarcity in developing countries and supporting land cover analysis.

Findings

BD-SAT supports training deep learning models with high accuracy.

Benchmark results demonstrate the dataset's effectiveness for five LULC classes.

Annotations are created using a rigorous, GIS-supported process.

Abstract

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.