arXiv AI August 6, 2026 By Kaiwen Xiao, Chunlong Fu, Liping Zheng, Yanfeng Su

TRNet: Topography-Guided Frequency Rectification and Structure-Aware Decoding for Multimodal Paddy Rice Segmentation

arXiv:2608. 04154v1 Announce Type: cross Abstract: Mapping paddy rice from very-high-resolution imagery in mountainous and hilly regions is difficult because terrain alters optical appearance and increases confusion with visually similar vegetation.

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arXiv AI

Sep 7

TRNet: Learning with Topographic Priors for VHR Paddy Rice Mapping

TRNet is a multimodal segmentation network that maps paddy rice in mountainous and hilly regions using 0.5 m RGB imagery, a 5 m DEM, and slope data. It introduces a Topographic Energy Spectral Rectification module to suppress steep‑slope clutter and a Topography Guided Paddy Structure Decoder to refine predictions with topographic context. On two test areas, TRNet achieves Rice IoU scores of 85.10 % and 80.68 %, outperforming a Dual Encoder U‑Net by 9.15 and 18.83 percentage points, and maintains strong performance on new August 2024 imagery.

By Kaiwen Xiao, Chunlong Fu, Liping Zheng, Yanfeng Su

computer-vision multimodal benchmarks

arXiv Machine Learning

Jul 27

Farmland Extent and Visible Boundary Mapping from 1 m NAIP Imagery Using Residual U-Net and Text-Prompted SAM 3 Refinement

arXiv:2607. 21881v1 Announce Type: cross Abstract: Agricultural field maps are often proprietary, incomplete, or outdated, yet they provide the spatial framework for crop monitoring, production accounting, and land-conversion analysis.

By Mohammadreza Narimani, Vikram Anand, Parastoo Farajpoor

Hugging Face Trending Papers

Aug 11

SAR2Agri: Learning SAR Intensity Representations for Agricultural Monitoring

Agricultural monitoring faces unique challenges, arising from the landscape's complex temporal, phenological, and climate dynamics, yet monitoring them is critical for ensuring food security. Synthetic Aperture Radar (SAR) satellites offer all-weather day-night imaging capability supporting key monitoring tasks including crop type mapping, yield prediction and phenological event detection.

multimodal benchmarks

arXiv Machine Learning

Sep 10

AAMBERS-UAV: Acquisition-Aware Multimodal Backbone Evaluation and Ranking for UAV Weedy Rice Segmentation

arXiv:2609.05762v1 Announce Type: cross Abstract: UAV image collections contain spatially and temporally related frames, yet semantic-segmentation benchmarks commonly split them at image level. Such...

By Tarek Rahman, Nazim-E-Alam, Md Kishor Morol, Jannatun Noor

computer-vision multimodal benchmarks

arXiv Computer Vision

Aug 31

Riverbank Erosion Analysis in Bangladesh Using Spatiotemporal Segmentation

arXiv:2510.17198v2 Announce Type: replace Abstract: Riverbank erosion is a serious environmental problem in Bangladesh, causing land loss, damage to infrastructure, and displacement of local communit...

By M. Saifuzzaman Rafat, Akif Islam, Mohd Ruhul Ameen, Momen Khandoker Ope, Abu Saleh Musa Miah, Jungpil Shin

computer-vision fine-tuning multimodal

arXiv AI

Jun 15

Clay-CNN Hybrids: Leveraging Geo-Foundational Models as Auxiliary Context for Landslide Detection

arXiv:2606. 14081v1 Announce Type: cross Abstract: Rapid post-event landslide mapping is essential for disaster response but remains difficult to automate due to extreme class imbalance.

By Huong Binh Vu

computer-vision fine-tuning benchmarks