Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architectures

• URL: http://arxiv.org/abs/2511.00073v1
• Date: Wed, 29 Oct 2025 12:32:28 GMT
• Title: Habitat and Land Cover Change Detection in Alpine Protected Areas: A Comparison of AI Architectures
• Authors: Harald Kristen, Daniel Kulmer, Manuela Hirschmugl,
• Abstract summary: We employ deep learning for change detection using long-term alpine habitat data from Gesaeuse National Park, Austria.<n>Clay v1.0 achieves 51% overall accuracy versus U-Net's 41% for multi-class habitat change, while both reach 67% for binary change detection.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Rapid climate change and other disturbances in alpine ecosystems demand frequent habitat monitoring, yet manual mapping remains prohibitively expensive for the required temporal resolution. We employ deep learning for change detection using long-term alpine habitat data from Gesaeuse National Park, Austria, addressing a major gap in applying geospatial foundation models (GFMs) to complex natural environments with fuzzy class boundaries and highly imbalanced classes. We compare two paradigms: post-classification change detection (CD) versus direct CD. For post-classification CD, we evaluate GFMs Prithvi-EO-2.0 and Clay v1.0 against U-Net CNNs; for direct CD, we test the transformer ChangeViT against U-Net baselines. Using high-resolution multimodal data (RGB, NIR, LiDAR, terrain attributes) covering 4,480 documented changes over 15.3 km2, results show Clay v1.0 achieves 51% overall accuracy versus U-Net's 41% for multi-class habitat change, while both reach 67% for binary change detection. Direct CD yields superior IoU (0.53 vs 0.35) for binary but only 28% accuracy for multi-class detection. Cross-temporal evaluation reveals GFM robustness, with Clay maintaining 33% accuracy on 2020 data versus U-Net's 23%. Integrating LiDAR improves semantic segmentation from 30% to 50% accuracy. Although overall accuracies are lower than in more homogeneous landscapes, they reflect realistic performance for complex alpine habitats. Future work will integrate object-based post-processing and physical constraints to enhance applicability.

Related papers

• Assessing the value of Geo-Foundational Models for Flood Inundation Mapping: Benchmarking models for Sentinel-1, Sentinel-2, and Planetscope for end-users [1.3877194435621216]
  Foundationsal Models (GFMs) enable fast and reliable extraction of information from satellite imagery.<n>Despite their potential, it remains unclear whether GFMs outperform traditional models like U-Net.<n>We evaluate three GFMs, Prithvi 2.0, Clay V1.5, DOFA, and UViT, against TransNorm, U-Net, and Attention U-Net.
  arXiv  Detail & Related papers  (2025-11-03T19:02:09Z)
• Balanced Multi-Task Attention for Satellite Image Classification: A Systematic Approach to Achieving 97.23% Accuracy on EuroSAT Without Pre-Training [0.0]
  This work presents a systematic investigation of custom convolutional neural network architectures for satellite land use classification.<n>We achieve 97.23% test accuracy on the EuroSAT dataset without reliance on pre-trained models.<n>Our approach achieves performance within 1.34% of fine-tuned ResNet-50 (98.57%) while requiring no external data.
  arXiv  Detail & Related papers  (2025-10-17T10:59:24Z)
• A Comparative Benchmark of Real-time Detectors for Blueberry Detection towards Precision Orchard Management [2.667064587590596]
  This study presents a novel comparative benchmark analysis of advanced real-time object detectors.<n>This dataset comprises 661 canopy images collected with smartphones during the 2022-2023 seasons.<n>Among the YOLO models, YOLOv12m achieved the best accuracy with a mAP@50 of 93.3%.
  arXiv  Detail & Related papers  (2025-09-24T21:42:24Z)
• FoBa: A Foreground-Background co-Guided Method and New Benchmark for Remote Sensing Semantic Change Detection [48.06921153684768]
  We present a new benchmark for remote sensing semantic change detection (SCD) called LevirSCD.<n>The dataset covers 16 change categories and 210 specific change types, with more fine-grained class definitions.<n>We propose a foreground-background co-guided SCD (FoBa) method, which leverages foregrounds enriched with contextual information to guide the model.<n>FoBa achieves competitive results compared to current SOTA methods, with improvements of 1.48%, 3.61%, and 2.81% in the SeK metric, respectively.
  arXiv  Detail & Related papers  (2025-09-19T09:19:57Z)
• Comparing remote sensing-based forest biomass mapping approaches using new forest inventory plots in contrasting forests in northeastern and southwestern China [6.90293949599626]
  Large-scale high spatial resolution aboveground biomass (AGB) maps play a crucial role in determining forest carbon stocks and how they are changing. GEDI is a sampling instrument, collecting dispersed footprints, and its data must be combined with that from other continuous cover satellites to create high-resolution maps. We developed local models to estimate forest AGB from GEDI L2A data, as the models used to create GEDI L4 AGB data incorporated minimal field data from China.
  arXiv  Detail & Related papers  (2024-05-24T11:10:58Z)
• Patch-Level Contrasting without Patch Correspondence for Accurate and Dense Contrastive Representation Learning [79.43940012723539]
  ADCLR is a self-supervised learning framework for learning accurate and dense vision representation. Our approach achieves new state-of-the-art performance for contrastive methods.
  arXiv  Detail & Related papers  (2023-06-23T07:38:09Z)
• Vision Transformers, a new approach for high-resolution and large-scale mapping of canopy heights [50.52704854147297]
  We present a new vision transformer (ViT) model optimized with a classification (discrete) and a continuous loss function. This model achieves better accuracy than previously used convolutional based approaches (ConvNets) optimized with only a continuous loss function.
  arXiv  Detail & Related papers  (2023-04-22T22:39:03Z)
• High-resolution landscape-scale biomass mapping using a spatiotemporal patchwork of LiDAR coverages [0.0]
  Estimating forest aboveground biomass at fine scales has become increasingly important for greenhouse gas estimation. Here we address common obstacles including selection of training data, the investigation of regional or coverage specific bias and error, and map patterns at multiple scales. Our model was overall accurate (% RMSE 13-33%), had very low bias (MBE $leq$ $pm$5 Mg ha$-1$), explained most field-observed variation.
  arXiv  Detail & Related papers  (2022-05-17T17:53:50Z)
• Country-wide Retrieval of Forest Structure From Optical and SAR Satellite Imagery With Bayesian Deep Learning [74.94436509364554]
  We propose a Bayesian deep learning approach to densely estimate forest structure variables at country-scale with 10-meter resolution. Our method jointly transforms Sentinel-2 optical images and Sentinel-1 synthetic aperture radar images into maps of five different forest structure variables. We train and test our model on reference data from 41 airborne laser scanning missions across Norway.
  arXiv  Detail & Related papers  (2021-11-25T16:21:28Z)
• G-DetKD: Towards General Distillation Framework for Object Detectors via Contrastive and Semantic-guided Feature Imitation [49.421099172544196]
  We propose a novel semantic-guided feature imitation technique, which automatically performs soft matching between feature pairs across all pyramid levels. We also introduce contrastive distillation to effectively capture the information encoded in the relationship between different feature regions. Our method consistently outperforms the existing detection KD techniques, and works when (1) components in the framework are used separately and in conjunction.
  arXiv  Detail & Related papers  (2021-08-17T07:44:27Z)
• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection [85.53263670166304]
  One-stage detector basically formulates object detection as dense classification and localization. Recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization.
  arXiv  Detail & Related papers  (2020-06-08T07:24:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.