Determination of land use and land cover change using multi-temporal PlanetScope images and deep learning CNN model

• Published in: Paddy and water environment Vol. 23; no. 3; pp. 405 - 423
• Main Author: Şimşek, Fatih Fehmi
• Format: Journal Article
• Language: English
• Published: Singapore Springer Nature Singapore 01.07.2025; Springer Nature B.V
• Subjects: Accuracy; Agricultural land; Agricultural policy; Agriculture; Arable land; Artificial neural networks; Biomedical and Life Sciences; Change detection; Classification; Climate change; Control systems; Deep learning; Ecotoxicology; Geoecology/Natural Processes; Grasslands; Ground truth; High resolution; Hydrogeology; Hydrology/Water Resources; Image classification; Image resolution; Land cover; Land resources; Land use; Legislation; Life Sciences; Machine learning; Monitoring; Neural networks; Reference systems; Resource management; Satellite imagery; Soil Science & Conservation; Spatial discrimination; Spatial resolution; Tree crops; Urban agriculture; Deep learning; LULC; LPIS; PlanetScope; Classification

Land use and land cover (LULC), an important component of Earth observation technology, plays a crucial role in image classification. Detailed LULC information is of great importance for monitoring agriculture and wetlands, urban and rural planning, land resource management, and the monitoring and d...