# Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks Source: [Semantic Segmentation of Medium-Resolution Satellite Imagery using Conditional Generative Adversarial Networks]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:48:52.480Z Authors: Aditya Kulkarni, Tharun Mohandoss, Daniel Northrup, Ernest Mwebaze, Hamed Alemohammad Published: 2020-12-05T18:18:45.000Z ## Abstract Semantic segmentation of satellite imagery is a common approach to identify patterns and detect changes around the planet. Most of the state-of-the-art semantic segmentation models are trained in a fully supervised way using Convolutional Neural Network (CNN). The generalization property of CNN is poor for satellite imagery because the data can be very diverse in terms of landscape types, image resolutions, and scarcity of labels for different geographies and seasons. Hence, the performance of CNN doesn't translate well to images from unseen regions or seasons. Inspired by Conditional Generative Adversarial Networks (CGAN) based approach of image-to-image translation for high-resolution satellite imagery, we propose a CGAN framework for land cover classification using medium-resolution Sentinel-2 imagery. We find that the CGAN model outperforms the CNN model of similar complexity by a significant margin on an unseen imbalanced test dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()