Volume 15, Number 5

Revolutionizing Land Cover Analysis: A Systematic Review of Geospatial Intelligence with Classification and Segmentation

  Authors

Vinothkumar Kolluru¹, Sudeep Mungara², Advaitha Naidu Chintakunta³, Lokesh Kolluru⁴, Charan Sundar Telaganeni⁵, ¹Senior Data Scientist, USA, ²Data Scientist, USA, ³Software Development Engineer, USA, ⁴Transportation Engineer, USA, ⁵Python Developer, USA

  Abstract

In today’s fast-paced technological landscape, artificial intelligence (AI) is revolutionizing industries, with geospatial analysis standing at the forefront of this transformation. The process of land cover classification, which involves categorizing different types of land surfaces—such as forests, urban areas, water bodies, and agricultural fields—has traditionally been plagued by inconsistencies and inaccuracies. These shortcomings have led to a variety of pressing real-world issues. Misclassified land cover data can result in the inefficient allocation of resources, where critical areas may be overlooked while less urgent regions receive attention. Additionally, the failure to accurately monitor land cover changes can allow illegal activities, such as deforestation, to go unnoticed, resulting in severe environmental degradation. Similarly, unmonitored topographical changes, like unauthorized construction projects, can significantly alter landscapes without regulatory oversight, posing risks to both the environment and public safety. Unchecked forest fires, exacerbated by delayed detection due to poor land cover classification, can spread rapidly, causing widespread damage. Furthermore, inaccurate monitoring of border fences can lead to security vulnerabilities and geopolitical tensions. Collectively, these issues contribute to the escalating challenges of climate change and urbanization, highlighting the critical need for more precise and reliable land cover classification methods. In response to these challenges, our study seeks to explore the potential of advanced machine learning (ML) techniques to revolutionize land cover classification. We leverage publicly available geospatial datasets, specifically EuroSAT and DeepGlobe, which provide comprehensive satellite imagery data across various regions. The focus of our research is on two primary tasks: Image Classification and Semantic Segmentation. Image Classification involves categorizing entire satellite images into specific land cover classes, providing a broad overview of the landscape. In contrast, Semantic Segmentation is a more granular approach that labels each pixel in an image according to its land cover class, offering detailed insights into the spatial distribution of different land types. To conduct a thorough analysis, we evaluate the performance of several cutting-edge models in these tasks. For Semantic Segmentation, we employ Meta AI’s Segment Anything Model (SAM), which is recognized for its ability to segment objects within images with high precision, and the U-Net architecture, a model that has been widely used in medical image analysis and has proven effective in various segmentation tasks. For Image Classification, we use the VGG and ResNet models, both of which are highly regarded in the field of computer vision for their capacity to extract detailed features from images and classify them with high accuracy. The primary objective of our research is to assess how these models perform when applied to land cover classification tasks and to identify their strengths and weaknesses in this specific context. By analyzing their performance, we aim to provide valuable insights that can guide future research efforts and help improve the accuracy and reliability of land cover classification methods. Additionally, our study seeks to highlight new opportunities for enhancing the monitoring and management of land resources, which is crucial for addressing the environmental and urbanization challenges that the world faces today. Our research aspires to contribute to the geospatial field by offering practical recommendations for utilizing machine learning techniques to improve land cover classification. By addressing the limitations of current methods and proposing more effective solutions, we hope to support the development of tools that are capable of accurately monitoring land cover changes and responding to the complex environmental and societal challenges of our time.