# Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions Source: [Minimum Class Confusion based Transfer for Land Cover Segmentation in Rural and Urban Regions]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:56.427Z Authors: Metehan Yal\\c{c}{\\i}n, Ahmet Alp K{\\i}nd{\\i}ro\\u{g}lu, Furkan Burak Ba\\u{g}c{\\i}, Ufuk Uyan, Mahiye Uluya\\u{g}mur \\"Ozt\\"urk Published: 2022-12-05T09:41:06.000Z ## Abstract Transfer Learning methods are widely used in satellite image segmentation problems and improve performance upon classical supervised learning methods. In this study, we present a semantic segmentation method that allows us to make land cover maps by using transfer learning methods. We compare models trained in low-resolution images with insufficient data for the targeted region or zoom level. In order to boost performance on target data we experiment with models trained with unsupervised, semi-supervised and supervised transfer learning approaches, including satellite images from public datasets and other unlabeled sources. According to experimental results, transfer learning improves segmentation performance 3.4% MIoU (Mean Intersection over Union) in rural regions and 12.9% MIoU in urban regions. We observed that transfer learning is more effective when two datasets share a comparable zoom level and are labeled with identical rules; otherwise, semi-supervised learning is more effective by using the data as unlabeled. In addition, experiments showed that HRNet outperformed building segmentation approaches in multi-class segmentation. ## 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]()