Template-Type: ReDIF-Article 1.0
Author-Name:Rimsha Jamil Ghilzai,Muhammad Abubakar Siddique, Sana Rubab, Kishwar Rasool, Soyab Sundas
Author-Email:darkshadowsrj@gmail.com
Author-Workplace-Name:Ghazi University D.G.Khan. Pakistan
Title:Advances in AI-Based Land Use and Land Cover Classification: A Review of Deep Learning and Remote Sensing Integration
Abstract:The integration of Artificial Intelligence (AI) with remote sensing has transformed Land Use  and  Land  Cover  (LULC)  classification,  enabling  more  accurate,  efficient,  and scalable  environmental  monitoring.  This  review  synthesizes  recent  advancements  in AI-driven  LULC  classification,  with  a  focus  on  deep  learning,  transfer  learning,  hybrid approaches,  and  explainable  AI  (XAI).  Recent  studies  demonstrate  that  AI  techniques significantly enhance classification accuracy and adaptability across diverse geospatial datasets, supporting   applications   such   as   urban   expansion   monitoring,   ecological   assessment, reforestation   analysis,   and   real-time   land   management.   Despite   these   advancements, challenges   remain   regarding   spectral   resolution,   model   interpretability,   computational efficiency, and data scarcity. This review highlights these limitations and discusses emerging solutions,  including  multimodal  data  fusion,  lightweight  AI  models,  and  scalable  MLOps frameworks. The findings provide insights for researchers, practitioners, and policymakers to guide future work in sustainable land management and environmental monitoring.
Keywords:Land Use and Land Cover (LULC); Remote Sensing; Artificial Intelligence (AI); Deep Learning; Machine Learning; Satellite Imagery; Image Classification
Journal: International Journal of Innovations in Science and Technology
Pages:2066-2090
Volume:7
Issue:3
Year: 2025
Month:August
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1563/2231
File-Format: Application/pdf
File-URL:https://journal.50sea.com/index.php/IJIST/article/view/1563
File-Format: text/html
Handle: RePEc:abq:IJIST:v:7:y:2025:i:3:p:2066-2090