Aitchison-Loss Training with Geospatial Embeddings Sharpens Compositional Land-Cover Maps

Multi-annual sub-pixel land cover mapping with TESSERA latent embeddings

GeoRGMAE: Geospatially Guided Masked Autoencoders for Building Segmentation

The proposed GeoRGMAE, a geospatially guided masked autoencoder pretraining strategy for building segmentation, introduces three masking strategies that prioritize semantically relevant building regions under the varying urban densities and suggests that incorporating geospatial priors into masked image modelling (MIM) can improve representation learning for downstream building segmentation tasks.

Land Cover Classification of Multi-Source Airborne Data using Conventional and Deep-Learning-Based Unsupervised Domain Adaptation

Abstract. For an increasing number of applications, land cover maps can be generated from remote sensing imagery using conventional and deep-learning-based semantic segmentation models. Relying on a large pool of training data, the networks struggle with the spatial-temporal-spectral heterogeneity in the complex and diverse remote sensing imageries, leading to a significant number of errors in the model predictions. This paper presents a workflow comprising domain adaptation and classification. In particular, we analyze two domain adaptation techniques: First, a conventional histogram-matching method, which has turned out to be a surprisingly fast and reliable tool in a previous study, and second, a CycleGAN, which we applied both in its standard form and with the perceptual loss, thereby penalizing style inconsistencies on deeper layers. By applying the workflow to three remote sensing datasets and six directions of domain adaptation, we show that there is “no free lunch” in the sense that all domain adaptation methods have their advantages. Depending on the dataset, classification method, and especially on the availability of 3D data, the performance gap can be reduced to up to 1.5% of the mean F1 score, demonstrating the soundness of the proposed method.

Zero-Shot Neural Architecture Search for National-Scale Forest Segmentation From Sentinel-2 Imagery

The results establish zero-shot NAS as a computationally efficient paradigm for large-scale Earth observation segmentation as a training-free strategy for semantic segmentation in Earth observation.

MTC-Net: Leveraging Multi-Temporal Consistency and Multi-View Synergistic Contrastive Learning for Remote Sensing Scene Classification

The remote sensing scene classification (RSSC) task plays a pivotal role in Earth observation missions, yet its progress remains constrained by the scarcity of high-quality labeled imagery. This article introduces a self-supervised learning (SSL) paradigm to address this challenge. First, for pseudo-label construction, a large set of long-interval satellite revisit imagery is collected and processed with pixel-level registration. The SIFT inliers retained during registration serve as saliency priors to guide asymmetric masking across views. This produces positive pairs that preserve global scene consistency while introducing controlled object-level ambiguities. Second, we propose a progressive layer-wise contrastive learning framework (MTC-Net) that couples the pseudo-label with the network’s representational hierarchy, forming a curriculum from local texture robustness to global semantic invariance. A dual-attention module with spatial–channel branches is further embedded to recalibrate intermediate features. The learning paradigm encourages the model to perform cross-view contextual reasoning rather than relying on pixel-wise correspondences. Experiments on three widely used datasets demonstrate that MTC-Net achieves competitive classification accuracy under limited-label settings, while ablation and visualization studies validate the effectiveness of establishing scene-level invariance through multi-temporal contrastive alignment.

SegFormer and SegFormer-UNet for anthropogenic geomorphic feature extraction from land surface parameters