Description: Geospatial anomaly detection refers to the techniques used to identify unusual or unexpected patterns in geospatial data, which contains information about the location and characteristics of phenomena on the Earth’s surface. This process is crucial in various applications, especially in environmental monitoring, where the aim is to detect changes that may indicate issues such as pollution, deforestation, or climate change. Artificial intelligence (AI) techniques play a fundamental role in this area, as they allow for the efficient and precise analysis of large volumes of geospatial data. Through machine learning algorithms, complex patterns can be identified that might go unnoticed with traditional methods. Geospatial anomaly detection not only enhances the response capacity to critical situations but also contributes to the sustainable planning and management of natural resources. In a world where geospatial information is generated at an accelerated pace, the ability to effectively detect anomalies becomes an invaluable tool for various stakeholders, including researchers, governmental bodies, and non-governmental organizations focused on environmental conservation and disaster management.
