Advances and Challenges in Ultra-High-Resolution Land Cover and Land Use Classification

Dear Colleagues,

Land cover and land use (LCLU) classification has witnessed significant advancements in recent decades, propelled by the widespread availability of high-resolution optical imagery and from satellite and unmanned aerial vehicles (UAVs). Land cover classification at spatial resolutions finer than 10 cm is commonly referred to as ultra-high-resolution (UHR), distinguishing it from very-high-resolution (VHR) classifications, which denote resolutions less than 1 m. The utilization of specific classification algorithms, diverse processing platforms, and increasingly powerful computational resources has further enhanced the capabilities of LCLU classification. Integrating very-high-resolution imagery has notably transformed LCLU analysis, presenting new opportunities and challenges.

In particular, challenges arise from significant inter-class variability, where pixels with markedly different digital numbers belong to the same class and spectrally similar but semantically distant classes. Various techniques have been employed to address these complexities, including object-based image analysis (OBIA) and incorporating texture-related information or object relationships with surrounding elements (spatial relations).

This Special Issue of Remote Sensing aims to compile contributions focused on generating land cover and land use maps using ultra-high-resolution and very-high-resolution optical data derived from both UAVs and satellites, along with their integration, including model transfer and model upscaling/downscaling. Submissions related to diverse geographic areas, specific semantic classifications, methodologies for minimizing errors associated with UHR and VHR resolution, and applications in complex scenarios are encouraged.

Dr. Elena Belcore
Prof. Dr. Marco Piras
Guest Editors

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