Artificial Intelligence (AI) has received tremendous attention from academia, industry, and the general public in recent years. The integration of geography and AI, or GeoAI, provides novel approaches for addressing a variety of problems in the natural environment and our human society. This entry briefly reviews the recent development of AI with a focus on machine learning and deep learning approaches. We discuss the integration of AI with geography and particularly geographic information science, and present a number of GeoAI applications and possible future directions.
Artificial intelligence is the study of intelligence agents as demonstrated by machines. It is an interdisciplinary field involving computer science as well as, various kinds of engineering and science, for example, robotics, bio-medical engineering, that accentuates automation of human acts and intelligence through machines. AI represents state-of-the-art use of machines to bring about algorithmic computation and understanding of tasks that include learning, problem solving, mapping, perception, and reasoning. Given the data and a description of its properties and relations between objects of interest, AI methods can perform the aforementioned tasks. Widely applied AI capabilities, e.g. learning, are now achievable at large scale through machine learning (ML), large volumes of data and specialized computational machines. ML encompasses learning without any kind of supervision (unsupervised learning) and learning with full supervision (supervised learning). Widely applied supervised learning techniques include deep learning and other machine learning methods that require less data than deep learning e.g. support vector machines, random forests. Unsupervised learning examples include dictionary learning, independent component analysis, and autoencoders. For application tasks with less labeled data, both supervised and unsupervised techniques can be adapted in a semi-supervised manner to produce accurate models and to increase the size of the labeled training data.
Object detection, the task of identifying and localizing objects within images or video, is a rapidly growing field that has become fundamental to numerous applications, including autonomous vehicles, surveillance systems, agricultural monitoring, and infrastructure assessment. While traditional visible-light cameras operating in the Red-Green-Blue (RGB) wavelengths excel at providing detailed color and texture information under good lighting conditions, they become ineffective in challenging conditions, such as low light, nighttime, or adverse weather. Long-Wave Infrared (LWIR) sensors complement these capabilities by detecting thermal radiation naturally emitted by objects, enabling detection regardless of ambient lighting conditions. Because of their complementary strengths, the fusion of RGB and LWIR modalities creates detection systems that maintain robust performance across diverse operational scenarios. Modern object detection leverages deep learning approaches, such as Convolutional Neural Networks (CNNs) and emerging transformer architectures, that have revolutionized the extraction of features and automatic classification of objects. This entry provides an overview of RGB and LWIR sensor technologies, neural network-based object detection methods, and their combined applications across domains, including autonomous driving, precision agriculture, infrastructure monitoring, maritime surveillance, and defense applications.