arXiv:2608. 10233v1 Announce Type: cross Abstract: Groundwater variability in Ghana remains poorly characterized due to limited long-term in-situ observations.
arXiv:2509.18123v2 Announce Type: replace Abstract: Accurate interpretation of soil moisture patterns is critical for irrigation scheduling and crop management, yet existing approaches for soil moist...
RIFT (Relative Isolation From Trees) is a deterministic anomaly detection method that builds a minimum spanning tree and scores points by the sum of apparent edge sizes from each point. For one-dimensional data, RIFT reproduces the closed-form limit of Isolation Forest exactly, while in higher dimensions it offers a parameter‑free, robust alternative that avoids axis‑parallel artifacts and handles varying density and clustered anomalies. An ensemble variant is proposed for large datasets, and experiments on synthetic data and the ADBench benchmark show comparable accuracy to Isolation Forest with reduced variance across random seeds.
arXiv:2607. 12454v1 Announce Type: new Abstract: Multivariate Time Series Anomaly Detection (MTSAD) is essential for reliability and safety in domains such as industrial process monitoring and financial risk management, yet conventional approaches rely on application-specific models that are costly to train and hard to scale.
The study presents a Teacher-Student machine‑learning framework that reconstructs high‑resolution groundwater depth maps across Bangladesh using sparse in‑situ measurements and GLDAS groundwater storage data. The model achieves high predictive accuracy (R² ≈ 0.93) and generalizes to unseen years and districts, revealing a nationwide decline in groundwater recharge of 1.10 mm/yr and widespread deepening of water tables. The findings challenge the notion of widespread recharge enhancement from pumping and highlight a significant imbalance between recharge declines and increases.
arXiv:2607. 21559v1 Announce Type: new Abstract: A consensus anomaly detection framework was applied to monthly malaria surveillance data from Ghana (2014-2023) to identify atypical transmission patterns.
arXiv:2608. 19801v1 Announce Type: new Abstract: Financial anomaly detection often relies on large unlabeled transaction logs, where anomalous samples may already be present during training.
arXiv:2606. 23833v1 Announce Type: new Abstract: Terrestrial water storage (TWS) integrates snow, soil moisture, surface water, and groundwater and is a key indicator of how climate variability and human activity reshape the global water cycle.
arXiv:2609.01514v1 Announce Type: cross Abstract: Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of che...
Historically, retrieving rainfall data from satellite imagery has been the domain of space agencies. However, in recent years, the development of cheaper, more compact satellites (SmallSats) capable o...
The paper investigates whether the performance of anomaly detection systems can be predicted without labeled anomalies. For kNN-based detectors, it derives a lower bound on AUC that links detection performance to the separation and variance of inlier and outlier scores, and uses this to analyze how density variation, intrinsic dimensionality, and domain mismatch affect score variability. The authors introduce pseudo‑anomaly probes that provide a reference for estimating relative score separation, and demonstrate through experiments on DCASE benchmarks that these probes enable anomaly‑free model selection to outperform conventional development‑set selection, especially under domain shift.
arXiv:2608. 12422v1 Announce Type: new Abstract: Two free satellite signals carry real information about glacial-lake outburst risk in the Nepal Himalaya: radar interferometry sees a moraine dam slowly sagging, and satellite weather marks the weeks when a primed lake is under stress.
The paper introduces ESIA, an Earth Surface Immune System that detects and recognizes unknown anomalies in satellite imagery without prior category knowledge. It uses a non‑specific innate stage for rapid localization and a specific adaptive stage that matches image patches to text prompts via a multi‑modal model, achieving high F1 scores. The system adapts to new scenes in seconds and has been validated on a large global dataset, with applications to farmland degradation after the Kakhovka Dam collapse and burn severity assessment from the 2025 Palisades Fire.