Water is a vital component of life that exists in a variety of forms, including oceans, rivers, ponds, streams, and canals. The automated methods for detecting, segmenting, and mapping surface water have improved significantly with the advancements in satellite imagery and remote sensing. Many strategies and techniques to segment water resources have been presented in the past. However, due to the variant width and complex appearance, the segmentation of the water channel remains challenging. Moreover, traditional supervised deep learning frameworks have been restricted by the scarcity of water channel datasets that include precise water annotations. With this in mind, this research presents the following three main contributions. Firstly, we curated a new dataset for water channel mapping in the Pakistani region. Instead of employing pixel-level water channel annotations, we used a weakly trained method to extract water channels from VHR pictures, relying only on OpenStreetMap (OSM) waterways to create sparse scribbling annotations. Secondly, we benchmarked the dataset on state-of-the-art semantic segmentation frameworks. We also proposed AUnet, an atrous convolution inspired deep learning network for precise water channel segmentation. The experimental results demonstrate the superior performance of the proposed AUnet model for segmenting using weakly supervised labels, where it achieved a mean intersection over union score of 0.8791 and outperformed state-of-the-art approaches by 5.90% for the extraction of water channels.

The center of human settlements is in the cities, which must have high‐quality habitats for their inhabitants. Many megachallenges of urbanization, population development, global advancement, environmental destruction, traffic management, and climate change must be addressed. This study is aimed at understanding how to maintain balanced land development in rapidly urbanizing towns to solve this challenge and mobility issues. Climate and weather forecasts, land cover, environmental indices, nonoptical and optical wavelengths, water history, and air quality are only some of the datasets available on Google Earth Engine, a publicly usable data repository. Machine learning techniques, i.e., random forest (RF), support vector machine (SVM), and classification and regression tree (CART), are used to monitor spatial‐temporal change regarding water, vegetation, and urbanization for Pakistan from 2013 to 2021 using Landsat 8. The detection of urban land suitability concerning multiple metrics such as ecological response variables, environmental tension, socio‐economic development potential, and natural resource potential is also found. Dataset features were classified as bands in the Google Earth Engine. Moreover, for 2020 and 2021, classification results showing the change in water, vegetation, and urbanization are also represented concerning China Pakistan Economic Corridor (CPEC) highway and the railway track to monitor and control traffic and its management.

Finger vein biometric technology has gained a lot of popularity over recent years. This is primarily due to the increased level of security and reliability that comes with it and also because it is non-intrusive in nature. This paper introduces a unique and lightweight image enhancement method for the purpose of finger vein identification using Convolutional Neural Networks (CNN). After finger detection, a methodology of Contrast Limited Adaptive Histogram Equalization (CLAHE) is performed on the image, which is followed by gamma correction. The image is then sharpened and median filtered. After that, the power-law is applied and then contrast adjustment is made. As a final step, CLAHE is used yet again to bring out the enhanced vascular structure. The method was appraising the four openly accessible different databases, which were examined as the most challenging available finger vein databases by many researchers. For recognition purposes, CNN was used with transfer learning. For the implementation of transfer learning, the architecture of VGG- 16 was modified to include 13 convolutional layers, five max-pooling layers, and 1 ReLU and one softmax layer. It was observed that with transfer learning, accuracy reached up to 99%, thus making it a highly accurate approach for finger vein recognition.