Crowdsourced spatial analytics is a rapidly developing field that involves collecting and
analyzing geographical data, utilizing the collective power of human observation. This paper …

The visual qualities of the urban environment influence people's perception and reaction to
their surroundings; hence the visual quality of the urban environment affects people's mental …

Abstract Machine learning can play a critical role in geospatial analysis, providing enhanced
computing efficiency, flexibility, and scalability, improved predictive capabilities, complicated …

Artificial neural networks (ANNs) are known to be powerful methods for many hard problems
(eg image classification, speech recognition or time series prediction). However, these …

Activities of identifying kinds of physical objects on lands from the images captured through
satellite and labeling them according to their usages are referred to as Land Use and Land …

Conventional classifiers are trained and evaluated using “balanced” data sets, in which all
classes are equally present. However, it is unlikely that the prior probability of occurrence …

Scientists use models to further their understanding of phenomena and inform decision-
making. A confluence of factors has contributed to an exponential increase in spatial data …

With recent advances in science and technology, more processing capability and data have
become available, allowing a more straightforward implementation of data analysis …

In this case study, we describe the mapping and change detection in the mangrove
coverage of Raigad District of Maharashtra, India, using remotely sensed images. The …

A significant quantity of sensor data has been used recently to construct a variety of Internet
of Things (IoT)-based methods as well as applications. They have been extensively …