mlsanityData to 14 Oct 2026

GeoMultiTaskNet: remote sensing unsupervised domain adaptation using geographical coordinates

arXiv 2304.07750 ↗First posted 16 Apr 2023cs.CVcited by 12

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Abstract (arXiv)

Land cover maps are a pivotal element in a wide range of Earth Observation (EO) applications. However, annotating large datasets to develop supervised systems for remote sensing (RS) semantic segmentation is costly and time-consuming. Unsupervised Domain Adaption (UDA) could tackle these issues by adapting a model trained on a source domain, where labels are available, to a target domain, without annotations. UDA, while gaining importance in computer vision, is still under-investigated in RS. Thus, we propose a new lightweight model, GeoMultiTaskNet, based on two contributions: a GeoMultiTask module (GeoMT), which utilizes geographical coordinates to align the source and target domains, and a Dynamic Class Sampling (DCS) strategy, to adapt the semantic segmentation loss to the frequency of classes. This approach is the first to use geographical metadata for UDA in semantic segmentation. It reaches state-of-the-art performances (47,22% mIoU), reducing at the same time the number of parameters (33M), on a subset of the FLAIR dataset, a recently proposed dataset properly shaped for RS UDA, used for the first time ever for research scopes here.

Key facts. Cited by 12 papers in the map (data through 14 Oct 2026). Builds on 44 of its 62 references that are in the map. First posted on arXiv 16 Apr 2023.

Builds on

44 of 62 references.

References · 44

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32 more
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Cited by year

Citing papers per year: 2024: 6; 2025: 6.

12 papers.

  1. 2025FLAIR-HUB: Large-scale Multimodal Dataset for Land Cover and Crop Mappingcited by 6
  2. 2024Learning from Unlabelled Data with Transformers: Domain Adaptation for Semantic Segmentation of High Resolution Aerial Imagescited by 4
  3. 2024Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mappingcited by 4
  4. 2024Cross-sensor self-supervised training and alignment for remote sensingcited by 3
  5. 2025AI Should Sense Better, Not Just Scale Bigger: Adaptive Sensing as a Paradigm Shiftcited by 3
  6. 2024Get Your Embedding Space in Order: Domain-Adaptive Regression for Forest Monitoringcited by 2
  7. 2025CoDEx: Combining Domain Expertise for Spatial Generalization in Satellite Image Analysiscited by 2
  8. 2025Deep Learning Based Domain Adaptation Methods in Remote Sensing: A Comprehensive Surveycited by 2
  9. 2024IrrNet: Advancing Irrigation Mapping with Incremental Patch Size Training on Remote Sensing Imagerycited by 1
  10. 2025Text-conditioned State Space Model For Domain-generalized Change Detection Visual Question Answeringcited by 1
  11. 2024Exploring selective image matching methods for zero-shot and few-sample unsupervised domain adaptation of urban canopy predictioncited by 0
  12. 2025SegDesicNet: Lightweight Semantic Segmentation in Remote Sensing with Geo-Coordinate Embeddings for Domain Adaptationcited by 0