DeepEarthMY: A Remote Sensing Dataset for Tropical Land-Cover Segmentation

DeepEarthMY: A Remote Sensing Dataset for Tropical Land-Cover Segmentation

Blog Article

Land-cover mapping is essential for applications such as urban planning, natural resource management, and environmental monitoring.However, tropical equatorial regions pose unique challenges to land-cover classification due to dense vegetation, persistent cloud cover, and spectral similarity across land-cover types.Moreover, existing land-cover classification models often perform poorly in these regions as they are primarily trained on datasets from non-equatorial climates.To address this gap, we introduce DeepEarthMY, a high-resolution land-cover mapping dataset specifically designed for equatorial regions.The dataset contains 4,007 meticulously annotated napoleon concealer images from 52 diverse regions across Malaysia, representing key land-cover types such as forests, buildings, roads, water bodies, agricultural lands, and barren land.

We evaluated the dataset by performing land-cover segmentation on selected state-of-the-art semantic segmentation models, including DC-Swin, HRNet, and UNetFormer.The experimental results reveal that the DC-Swin model achieves the best performance, with a mIoU score of 80.23%.To further evaluate the significance of region-specific datasets for land-cover classification, we performed cross-dataset testing on two datasets: DeepEarthMY from the equatorial region and LoveDA from the temperate climate, using five-fold cross-validation.HRNet models trained on DeepEarthMY achieved a mIoU of 77.

2% on cga 200 to cga 510 adapter DeepEarthMY but only 33.2% on the LoveDA test set.Conversely, models trained on LoveDA achieved 51.4% and 43% on the LoveDA and DeepEarthMY test sets, respectively.This significant performance gap highlights the need for region-specific datasets to advance land-cover research, particularly in equatorial climates where land-cover datasets are scarce.

In conclusion, the DeepEarthMY dataset can be an invaluable resource for remote sensing in the equatorial region.The dataset is available at https://zenodo.org/records/14242124.