Abstract：
In the context of environmental and social applications, the analysis of land use and land cover (LULC) holds immense significance. The growing accessibility of remote sensing (RS) data has led to the development of LULC benchmark datasets, especially pivotal for intricate image classification tasks. This study addresses the scarcity of such benchmark datasets across diverse settings, with a particular focus on the distinctive landscape of India. The study entails the creation of patch-based datasets, consisting of 4000 labelled images spanning four distinct LULC classes derived from Sentinel-2 satellite imagery. For the subsequent classification task, three traditional machine learning (ML) models and three convolutional neural networks (CNNs) were employed. Despite facing several challenges throughout the process of dataset generation and subsequent classification, the CNN models consistently attained an overall accuracy of 90% or more. Notably, one of the ML models stood out with 96% accuracy, surpassing CNNs in this specific context. The study also conducts a comparative analysis of ML models on existing benchmark datasets, revealing higher prediction accuracy when dealing with fewer LULC classes. Thus, the selection of an appropriate model hinges on the given task, available resources, and the necessary trade-offs between performance and efficiency, particularly crucial in resource-constrained settings. The standardized benchmark dataset contributes valuable insights into the relative performance of deep CNN and ML models in LULC classification, providing a comprehensive understanding of their strengths and weaknesses.
摘要：
在环境和社会应用的背景下，土地利用和土地覆盖分析(LULC)具有极其重要的意义。遥感（RS）数据的可及性不断增长，导致了LULC基准数据集的发展，对于复杂的图像分类任务尤其重要。这项研究解决了跨不同环境的此类基准数据集的稀缺性，特别关注印度独特的景观。这项研究需要创建基于补丁的数据集，由4000张标记的图像组成，这些图像跨越了从Sentinel-2卫星图像得出的四个不同的LULC类别。对于后续的分类任务，采用了三种传统的机器学习(ML)模型和三种卷积神经网络(CNN)。尽管在数据集生成和后续分类的整个过程中面临着一些挑战，CNN模型始终达到90%或更高的总体准确率。值得注意的是,其中一个ML模型以96%的准确率脱颖而出，在这个特定的背景下超越CNN。该研究还对现有基准数据集上的ML模型进行了比较分析，在处理较少的LULC类时显示更高的预测精度。因此，选择合适的模型取决于给定的任务，可用资源,以及性能和效率之间的必要权衡，在资源受限的环境中尤其重要。标准化的基准数据集有助于深入CNN和ML模型在LULC分类中的相对性能，全面了解他们的长处和短处。
