The paper compares classical machine learning algorithms—such as Logistic Regression, SVM, Random Forest, XGBoost, and CatBoost—with Tabular Deep Learning models (TabNet, FT-Transformer, TabTransformer, TabSeq, and 1D CNNs) for urban land cover classification using a UCI dataset derived from high‑resolution aerial imagery. It evaluates performance across nine land cover classes, addressing challenges like high dimensionality, heterogeneous features, and class imbalance by applying weighted cross‑entropy loss for deep models and measuring accuracy, macro‑precision, macro‑recall, macro‑F1, AUC‑ROC, and confusion matrices. Results indicate that while tree ensembles remain strong baselines, Tabular Deep Learning can match or surpass them when non‑linear interactions are prominent and imbalance handling is effective.
arXiv:2607. 23024v1 Announce Type: cross Abstract: High-resolution satellite imagery is the backbone of good land-cover classification, and without that, environmental monitoring, urban planning, and sustainable resource management all fall short.
arXiv:2606. 17403v1 Announce Type: cross Abstract: Rapid assessment of building damage from satellite imagery is essential for effective disaster response and recovery.
Timely, high-resolution maps of flood extent around settlements are essential for emergency response and damage assessment. We consider airborne RGB imagery for flood mapping as it can be collected rapidly at low cost.
Over the past decade, interest in applying machine learning (ML) to automate forest monitoring has grown significantly. However, existing training datasets are predominantly drawn from North America, Europe, Asia, and Australia, leaving a critical gap in African forestry data.
The study presents a new approach to detect Christmas tree plantations in high‑resolution aerial imagery, treating the task as a rare‑target semantic segmentation problem. It introduces a Hard Negative Mining strategy that significantly improves precision‑recall performance, achieving an IoU of 0.733 and an F1‑score of 0.846 on a 2020 test set. Temporal transfer experiments demonstrate the model’s ability to generalize across years, while large‑scale validation highlights the challenge posed by the plantations’ small spatial footprint.
arXiv:2608. 05265v1 Announce Type: new Abstract: Prediction of post-wildfire debris flows is critical for mitigating hazards to communities, infrastructure, and resources during intense rainfall in recently burned areas.
The paper introduces a PyTorch library that unifies 55 hyperspectral image models across six deep‑learning paradigms, providing a common registry, automatic tensor adaptation, and standardized constructors. It includes 24 benchmark scenes from various sensors, with tools for data preprocessing, patch extraction, and rigorous train‑test partitioning to avoid overlap. Experiments on 1,320 model‑scene combinations show that scene difficulty outweighs architecture choice, with no single paradigm dominating and small models performing competitively with much larger ones.
Hyperspectral remote sensing has advanced across diverse deep learning paradigms, including spectral spatial CNNs, Vision Transformers, Mamba, graph neural networks, Kolmogorov Arnold networks, and se...
The technical report introduces Hyperspectral Image Models, a modular framework that unifies 55 deep‑learning models across six paradigms for hyperspectral remote sensing. It standardizes tensor conventions, evaluation protocols, and dataset handling, integrating 24 benchmark scenes from various sensors and providing tools to avoid train‑test overlap. Experiments across 1,320 model‑scene combinations show that scene difficulty outweighs architecture, with no single paradigm dominating and small models achieving performance comparable to much larger ones.
The paper presents a framework that enhances deep‑learning tree‑cover mapping in New South Wales by fusing multiple imagery sources and normalizing image quality. It introduces an image‑composition technique that removes defects and a prediction‑fusion method that reduces reliance on any single image, together cutting errors by 38.2 % and 53.6 % respectively. Label transfer across diverse imagery further boosts data efficiency, yielding error reductions of 28.1 %–76.2 % and a 13‑fold decrease in performance variability across dates.
Hyperspectral remote sensing has advanced across diverse deep learning paradigms, including spectral spatial CNNs, Vision Transformers, Mamba, graph neural networks, Kolmogorov Arnold networks, and se...
arXiv:2609.21903v1 Announce Type: new Abstract: Leaf-wood segmentation of individual trees from LiDAR point clouds is essential for quantitative structure models (QSMs) used in non-destructive biomas...