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Sep 2026
Improving digital soil mapping for imbalanced classes through feature selection and resampling
Accurate classification of imbalanced soil data remains a key challenge in Digital Soil Mapping (DSM). We propose a two-step preprocessing strategy that integrates feature selection with resampling to enhance soil class prediction. Using 140 soil profiles from the Darab region (Iran), four feature selection methods (...
Vahideh Sadeghizadeh, Seyed Mohammad Ali Abtahi, M. Baghernejad et al.
· Scientific Reports · 0 citations
Open access
Sep 2026
Multi-Temporal Phenology–Spectral Feature Optimization and Decision Tree Classification for Estuarine Wetland Vegetation Mapping
This research introduces a Google Earth Engine (GEE)-based hierarchical framework for mapping eight typical vegetation types in the Liaohe Estuary using Sentinel-2 imagery from 2023 to 2025, indicating that seasonal phenological metrics offer superior discrimination of vegetation types compared to peak-summer spectral...
Yi-Han Wang, Jin-Xiu Zeng, Ruo-Zeng Wang et al.
· Remote Sensing · 0 citations
Open access
Sep 2026
CBERS 4A/WPM Image Classification for Mapping Land Use and Land Cover in TCRAs
Tropical restoration areas present high spectral heterogeneity and spatial fragmentation, making land use and land cover (LULC) classification challenging, particularly when using medium-resolution satellite imagery. Although spectral mixture analysis, object-based image analysis, and machine learning approaches have b...
Carla Rodrigues Santos, F. Salazar, F. B. J. R. Dallaqua et al.
· Geographies · 0 citations
Open access
Oct 2026
Machine learning analysis of land use change dynamics in Malawi’s Chia Lagoon
Land-use and land-cover (LULC) change is a major driver of ecosystem degradation, particularly in freshwater wetlands where spatially explicit evidence is limited. This study aimed to quantify two decades (2004–2024) of LULC change in Malawi’s Chia Lagoon catchment using machine-learning-based remote sensing approaches...
J. Nyengere, H. Tholo, Cosmas Makumba et al.
· BMC Environmental Science · 0 citations
Open access
2026
Comparative Assessment of Machine Learning Classifiers for Land Use/Land Cover Mapping in a Tropical Savanna Landscape
Accurate land use/land cover (LULC) information is essential for environmental monitoring, agricultural planning, and sustainable land management, particularly in rapidly changing tropical savanna landscapes. This study comparatively assessed the performance of Random Forest (RF), Support Vector Machine (SVM), and Clas...
Y. A. Awwal, M. Isma'il, S.-L. Ya'u et al.
· Kaduna Journal of Geography · 0 citations
Open access
Aug 2026
High-Resolution Mapping of Forest Vegetation Types Using Multiplatform Imagery and Advanced Classification Techniques
Accurate and up-to-date information is essential for environmental monitoring, particularly in regions characterized by complex topography and heterogeneous landscapes. This study presents a multisource remote sensing–based approach for forest vegetation classification on La Palma Island (Canary Islands, Spain), which...
Javier Marcello, F. Eugenio, A. Mederos-Barrera et al.
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