Land Use Mapping with Deep Learning and Remote Sensing
The Motivation: Understanding the distribution of various land cover types is essential for informed decision-making related to land management and urban planning. With the diverse array of urban, rural, and natural environments on the island of Cyprus, accurate mapping of land use and land cover (LULC) patterns is crucial for conservation, development, and urban planning.
The Method: Our team of researchers utilized high-resolution remote sensing imagery and deep learning techniques to map the LULC patterns of the island of Cyprus. The deep learning model was trained to recognize 19 different LULC classes, including agricultural land, urban areas, rural areas, green spaces, and forests. The source of data used for this project was high spatial resolution satellite imagery with a resolution of 0.3-0.5 meters/pixel. The model was fine-tuned using transfer learning techniques to improve its accuracy on high-resolution remote sensing imagery.
The Results: After rigorous testing and evaluation, the team produced a comprehensive LULC map as a GIS layer that covered the entire island of Cyprus. The map provided detailed information on the distribution of LULC classes over 9,000 square kilometers. Our deep learning model achieved an impressive recall value of 85% and an intersection over union (IoU) of 75%.
The Importance: The LULC map has important implications for land management and planning on the island of Cyprus. The map can be used to inform decisions related to urbanization, natural resource management, and biodiversity conservation. For example, the LULC map can be used to identify areas suitable for development or to prioritize conservation efforts in areas of high ecological significance.
The Perspective: The success of this project highlights the effectiveness of deep learning and remote sensing techniques in mapping LULC patterns in complex landscapes. The high recall value and IoU achieved by our model demonstrate its potential for accurate and detailed LULC mapping on other landscapes as well. Our team of researchers has created a comprehensive
The Perspective: The success of this project highlights the effectiveness of deep learning and remote sensing techniques in mapping LULC patterns in complex landscapes. The high recall value and IoU achieved by our model demonstrate its potential for accurate and detailed LULC mapping on other landscapes as well. Our team of researchers has created a comprehensive LULC map of the island of Cyprus, providing valuable information for land management, planning, and decision-making. We hope that this work inspires others to pursue innovative solutions for addressing environmental and social challenges through deep learning and remote sensing techniques.