DL4EO: A Unified Python Framework for Preparing Multi-Source Remote Sensing Data for any Segmentation Task

Abstract

Deep Learning (DL) has transformed Earth Observation (EO) tasks such as vegetation mapping, building detection, and water body monitoring by enabling fast and reliable spatiotemporal analysis. Yet, global-scale applications remain limited due to the un-availability of readily available datasets and complexity involved in processing planetary scale datasets. To address this, we present DL4EO, a Python package that simplifies the preparation of remote sensing data for deep learning. It automatically downloads and processes Sentinel-1 (RTC), Sentinel-2 (Level 2C), and Copernicus DEM data, generating consistent image-label pairs of user-defined size without requiring credentials. As an input, package requires a base directory, date range, cloud cover threshold, area of interest (AOI), and a shapefile of the target feature. To acquire Sentinel-1 RTC data, DL4EO selects the closest available image based on the corresponding Sentinel-2 acquisition. It handles projection, resampling, and alignment to produce globally consistent datasets. The final output consists of user define size (e.g., 256×256) images with 11 bands: Blue, Green, Red, NIR, SWIR1, SWIR2, spectral band (e.g., Normalized Difference Water Index: NDWI), Slope, Elevation, VV, and VH and corresponding label mask, target feature with pixel value 1 and rest 0. This fully automated dataset preparation pipeline supports scalable and efficient deep learning applications in Earth observation.