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 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.
arXiv:2604. 13240v2 Announce Type: replace-cross Abstract: Mapping the spatial distribution of species is essential for conservation policy and invasive species management.
arXiv:2608.24594v1 Announce Type: new Abstract: Submerged kelp forests are vital coastal ecosystems that support marine biodiversity and ecosystem dynamics, yet accurate underwater kelp segmentation...
The study evaluates crop‑yield forecasting methods for the 2012 Midwestern US drought, comparing non‑deep learning machine learning models with a deep learning model (VITA) using 16 meteorological predictors. It highlights challenges such as distributional dissimilarity between training and test data, spatial and temporal sparsity, and demonstrates that sample weighting and feature selection improve non‑deep learning models but not VITA. The work contrasts deep versus non‑deep learning approaches and shows how modifications can mitigate issues arising from extreme drought conditions.
The paper presents a deep‑learning crater detection algorithm (CDA) based on the OWLv2 Vision Transformer, fine‑tuned with Low‑Rank Adaptation on a manually labeled IMPACT dataset. It optimizes a combined loss of CIoU for localization and contrastive loss for classification, achieving a maximum recall of 92.6% and precision of 71.4% on lunar images. The method demonstrates reliable crater detection under varied illumination and rugged terrain, supporting safer lunar landings.
arXiv:2511. 15941v2 Announce Type: replace-cross Abstract: Tabular data underpins decisions across science, industry, and public services.