J. Nalini

Building object extraction plays a crucial role in the monitoring and strategic planning of urban infrastructures. With the rapid advancement of deep learning (DL), segmentation tasks are becoming more effective, even though models trained on datasets from different regions may not perform well in Indian areas due to their complex, unstructured cityscapes. Therefore, this paper introduces a novel building object segmentation dataset, namely, NITK Computer Vision and Deep Learning (NCVDL) Cartosat-2S building dataset, which covers three diverse regions of India. Moreover, this paper presents a lightweight DL model, namely, the KAN Building Detection Network (KANBDNet), for extracting buildings from satellite images, aiming to reduce the number of parameters without compromising performance using Kolmogorov–Arnold Networks, and the training is guided by a boundary-focused mIoU metric. It is mainly structured using three modules: a split-wise feature extraction module to efficiently extract features by splitting them into two and then process separately, a dense multiscale feature extraction block to extract and densely connect features at various scales, and a skip layer which effectively combines the encoder stage and high level features to enhance the features passing through the decoder. The performance of the proposed KANBDNet and state-of-the-art (SOTA) DL-based building segmentation models is evaluated using the new NCVDL Cartosat-2S dataset and the publicly available GF-7 building dataset. The proposed KANBDNet outperforms all SOTA models, achieving ( F 1 -score, kappa coefficient, jaccard index, dice coefficient) of (87.75%, 75.50%, 78.87%, 80.69%) and (92.10%, 84.19%, 85.83%, 86.62%) on the NCVDL Cartosat-2S and GF-7 building datasets, respectively.

Deep learning (DL) algorithms are currently the most effective methods for change detection (CD) from high-resolution multi-spectral (MS) remote-sensing (RS) images. Because a variety of satellites are able to provide a lot of data, it is now easy to find out changes using efficient DL models. Current CD methods focus on simple structure and combining the features obtained by all the stages together rather than extracting multi-scale features from a single stage since it may lead to information loss and imbalance contribution of features at different stages. This in turn results in misclassification of small changed areas, poor edge and shape preservation of changed areas. This paper introduces an Enhanced RSCD network (ERSCDNet) for CD from bi-Temporal (BT) aerial and MS images. The proposed encoder-decoder based ERSCDNet model uses an attention based encoder and decoder blocks and a Modified New Spatial Pyramid Pooling block (MNSPP) at each stage of the decoder part which effectively utilize features at each encoder stages and prevent information loss. The Learning, Vision and Remote Sensing - CD (LEVIR-CD), Onera Satellite Change Detection (OSCD) and Sun Yat-Sen University - CD (SYSU-CD) datasets are used to evaluate the ERSCDNet model. The ERSCDNet gives better performance than all the models used in this paper for comparison. It gives a F1 Score (F1), a Kappa Coefficient (Ka), and a Jaccard Index (JI) of (0.9306, 0.9282, 0.8703), (0.8945, 0.8887, 0.8091) and (0.7581, 0.6876, 0.6103) on OSCD, LEVIR-CD and SYSU-CD datasets respectively.