BD Open LULC Map: High-resolution land use land cover mapping & benchmarking for urban development in Dhaka, Bangladesh
Mir Sazzat Hossain, Ovi Paul, Md Akil Raihan Iftee, Rakibul Hasan Rajib, Abu Bakar Siddik Nayem, Anis Sarker, Arshad Momen, Md. Ashraful Amin, Amin Ahsan Ali, AKM Mahbubur Rahman

TL;DR
This paper introduces the BD Open LULC Map, a high-resolution, pixel-wise land use land cover dataset for Dhaka, Bangladesh, created to improve urban mapping and benchmarking in data-scarce developing regions.
Contribution
The work provides a large-scale, validated LULC dataset for Dhaka using high-resolution imagery, and benchmarks deep learning segmentation models across multiple satellite sources.
Findings
DeepLab V3+ achieves high accuracy on the dataset.
BOLM enables improved urban land cover classification.
Performance varies between Bing and Sentinel-2A imagery.
Abstract
Land Use Land Cover (LULC) mapping using deep learning significantly enhances the reliability of LULC classification, aiding in understanding geography, socioeconomic conditions, poverty levels, and urban sprawl. However, the scarcity of annotated satellite data, especially in South/East Asian developing countries, poses a major challenge due to limited funding, diverse infrastructures, and dense populations. In this work, we introduce the BD Open LULC Map (BOLM), providing pixel-wise LULC annotations across eleven classes (e.g., Farmland, Water, Forest, Urban Structure, Rural Built-Up) for Dhaka metropolitan city and its surroundings using high-resolution Bing satellite imagery (2.22 m/pixel). BOLM spans 4,392 sq km (891 million pixels), with ground truth validated through a three-stage process involving GIS experts. We benchmark LULC segmentation using DeepLab V3+ across five major…
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Taxonomy
TopicsLand Use and Ecosystem Services · Remote Sensing and Land Use · Soil and Land Suitability Analysis
