Miti360 is a new annotated aerial and terrestrial imagery dataset from a Kenyan reforestation site that supports ML models for tree detection, species mapping, and growth modeling in Africa.
State -of- the-Art in Photogrammetry, Remote Sensing and Spatial Information Science,
8 Pith papers cite this work, alongside 24 external citations. Polarity classification is still indexing.
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CloudLULC-Net is an end-to-end heterogeneous SAR-optical fusion network for LULC mapping under cloud contamination that achieves 86.60% OA, 83.29% F1, and 73.51% mIoU on a new global benchmark of 40,223 samples.
GeoNatureAgent Benchmark tests seven LLMs on 93 tasks via a production geospatial API, with Claude Sonnet 4 at 60.8% and DeepSeek V3.2 offering near performance at 11x lower cost while all models fail on close-value comparisons.
GS-QA is a new benchmark of 2,800 QA pairs on 28 templates using OSM and Wikipedia data to evaluate LLMs on spatial predicates, multi-source reasoning, and diverse answer types including distances and counts.
Self-supervised pretraining of Point-M2AE on ShapeNet-55 plus 2,400 tree point clouds improves cross-site leaf-wood segmentation and halves QSM-derived volume estimation error versus algorithmic baselines.
SCORE is a sequential cyclic optimization method for continuous ground station placement that achieves up to 15% higher total downlink throughput than fixed-site approaches and converges with up to 5x fewer evaluations than differential evolution on tested constellations.
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
SAM achieves ~58% accuracy delineating field boundaries from SkySat imagery without training, with gains from multi-date inputs and varied sizes, establishing proof-of-concept for data-scarce agriculture mapping.
citing papers explorer
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Miti360: A Comprehensive Dataset for Improved Reforestation Monitoring
Miti360 is a new annotated aerial and terrestrial imagery dataset from a Kenyan reforestation site that supports ML models for tree detection, species mapping, and growth modeling in Africa.
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Heterogeneous SAR-optical fusion for near-real-time land use and land cover mapping under cloud contamination: A novel framework and global benchmark dataset
CloudLULC-Net is an end-to-end heterogeneous SAR-optical fusion network for LULC mapping under cloud contamination that achieves 86.60% OA, 83.29% F1, and 73.51% mIoU on a new global benchmark of 40,223 samples.
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GeoNatureAgent Benchmark: Benchmarking LLM Agents for Environmental Geospatial Analysis Across Frontier and Open-Weight Foundation Models
GeoNatureAgent Benchmark tests seven LLMs on 93 tasks via a production geospatial API, with Claude Sonnet 4 at 60.8% and DeepSeek V3.2 offering near performance at 11x lower cost while all models fail on close-value comparisons.
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GS-QA: A Benchmark for Geospatial Question Answering
GS-QA is a new benchmark of 2,800 QA pairs on 28 templates using OSM and Wikipedia data to evaluate LLMs on spatial predicates, multi-source reasoning, and diverse answer types including distances and counts.
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Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
Self-supervised pretraining of Point-M2AE on ShapeNet-55 plus 2,400 tree point clouds improves cross-site leaf-wood segmentation and halves QSM-derived volume estimation error versus algorithmic baselines.
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Free-Placement Optimization of Ground Station Locations for Low-Earth Orbit Satellites
SCORE is a sequential cyclic optimization method for continuous ground station placement that achieves up to 15% higher total downlink throughput than fixed-site approaches and converges with up to 5x fewer evaluations than differential evolution on tested constellations.
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Flow matching for Sentinel-2 super-resolution: implementation, application, and implications
Flow matching achieves single-step pixel accuracy and 20-step perceptual quality for Sentinel-2 super-resolution, outperforming diffusion and Real-ESRGAN while enabling large-scale 2.5 m land-cover products.
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Investigating the Segment Anything Foundation Model for Mapping Smallholder Agriculture Field Boundaries Without Training Labels
SAM achieves ~58% accuracy delineating field boundaries from SkySat imagery without training, with gains from multi-date inputs and varied sizes, establishing proof-of-concept for data-scarce agriculture mapping.