Abstract: Semantic segmentation of remote sensing images is crucial for disaster monitoring, urban planning, and land use. Due to scene complexity and multiscale features of targets, semantic ...
Semantic segmentation of remote sensing images is pivotal for comprehensive Earth observation, but the demand for interpreting new object categories, coupled with the high expense of manual annotation ...
Abstract: Variations in scene complexity and image quality across remote sensing images lead to inconsistent performance when applying pretrained semantic segmentation models. To ensure quality ...
Sometimes, reading Python code just isn’t enough to see what’s really going on. You can stare at lines for hours and still miss how variables change, or why a bug keeps popping up. That’s where a ...
Research has focused on Multi-Modal Semantic Segmentation (MMSS), where pixel-wise predictions are derived from multiple visual modalities captured by diverse sensors. Recently, the large vision model ...
Introduction: Weeds compete with crops for water, nutrients, and light, negatively impacting maize yield and quality. To enhance weed identification accuracy and meet the requirements of precision ...
The Python team at Microsoft is continuing its overhaul of environment management in Visual Studio Code, with the August 2025 release advancing the controlled rollout of the new Python Environments ...
Introduction: Rising global populations and climate change necessitate increased agricultural productivity. Most studies on rice panicle detection using imaging technologies rely on single-time-point ...
Modern software engineering faces growing challenges in accurately retrieving and understanding code across diverse programming languages and large-scale codebases. Existing embedding models often ...
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