Guiding Deep Learning with landscape metrics for LULC mapping applications
Abstract: Deep Learning (DL) has become a core methodology in GeoAI and remote sensing, yet many models still lack explicit integration of geographic principles, often leading to computationally expensive and geographically implausible predictions. This work investigates how geographic knowledge can...
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| Autori principali: | , , , |
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| Natura: | Chapter/Article Conference Paper |
| Lingua: | inglese |
| Pubblicazione: |
May 20, 2026
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| Edizione: | Version 1.0 |
| In: |
Zenodo
Year: 2026, Pages: 1-6 |
| DOI: | 10.5281/zenodo.20309544 |
| Accesso online: | Verlag, kostenfrei, Volltext: https://doi.org/10.5281/zenodo.20309544 Verlag, kostenfrei, Volltext: https://zenodo.org/records/20309544 
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| Note sull'autore: | Nikolaos Kolaxidis, Christina Ludwig, Maciej Adamiak, and Alexander Zipf |
| Riassunto: | Abstract: Deep Learning (DL) has become a core methodology in GeoAI and remote sensing, yet many models still lack explicit integration of geographic principles, often leading to computationally expensive and geographically implausible predictions. This work investigates how geographic knowledge can be integrated into deep learning models as a regularization signal. Using landscape metrics derived from OpenStreetMap, we show that these metrics capture structural similarities and differences across regions and can serve as transferable geographic references. We propose a two head architecture based on SegFormer, combining semantic segmentation with a regression head that predicts landscape metrics from the segmentation output. A composite loss integrates cross entropy with a landscape metrics based regularization term to guide training. Initial experiments demonstrate stable convergence while highlighting challenges in computational efficiency and loss design. Overall, this work represents a first step toward GIScience-guided DL, emphasizing spatial structure and geographic context for more interpretable and sustainable models. Keywords: giscience-guided deep learning, landscape metrics, remote sensing, LULC, Open-StreetMap |
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| Descrizione del documento: | Gesehen am 12.08.2025 |
| Descrizione fisica: | Online Resource |
| DOI: | 10.5281/zenodo.20309544 |