Improving GIS maps with AI: Field Boundary Detection
Thanks to the development and increased computing power, humanity has opportunities that previously could only be dreamed of. Many of these new opportunities are related to artificial intelligence technologies, machine learning, and deep learning.
Geographic information systems depend highly on computing power, especially 3D calculations, map displays, and route options. The combination of AI and GIS technologies (especially technology in agriculture) also transforms industries through practical applications. It plays a vital role in improving the resilience of sectors such as forestry and agriculture and enhancing and improving management in healthcare, banking, insurance, logistics, and many others.
AI and GIS Mapping
The capabilities of AI in performing various tasks have evolved in recent years. The technology surpasses human accuracy in image recognition. The accuracy of text translation and reading comprehension has also improved significantly. The combination of AI and GIS Maps is gradually becoming the main focus of research and applications in geosciences. GIS uses the power of AI in geographic visualization as well as spatial analysis. It’s also possible to process and mine data when confronted with AI recognition findings.
Map visualization has many applications, including traffic monitoring and city component management. Thus, decision-makers receive information conveniently and intuitively. AI technologies, especially machine learning, are a strategic investment for many companies.
Location data is used to gain a competitive advantage through improved forecast accuracy and process automation. Such benefits reveal hidden trends and patterns. In addition, AI can significantly improve supply chains and logistics in general. With these technologies, retailers can select the most appropriate locations, provide customer support and more personalized services, and customize advertising based on location.
Field Boundary Mapping
In the 21st century, agriculture faces a significant challenge due to population growth and the need to meet food demand sustainably. Defining field boundaries plays an important role in the sustainable development of agriculture. This feature helps to reduce the negative impact on nature and biodiversity by limiting the entry of pesticides and fertilizers into habitats and beyond agricultural land.
Players in the agricultural technology market, such as EOS Data Analytics, which provides satellite data analytics worldwide, understand the problems of modern agriculture and provide effective solutions. The company has developed an all-in-one EOSDA Crop Monitoring platform for precision farming that combines satellite imagery analytics with AI algorithms.
EOSDA uses convolutional neural networks and AI technologies as part of its own innovative field boundaries detection method. This method’s application makes it possible to detect arable land automatically, mark the farm’s boundaries on the map and calculate the size of the cultivated area. These data improve the classification accuracy of crops grown on large farms.
The company’s team of experienced scientists uses high-resolution images and powerful algorithms to determine the boundaries. This combination provides a high level of detail. This solution provides the ability to detect changes in field contours. Users can compare current boundaries with existing databases. A noteworthy advantage is the compatibility of EOSDA boundary map formats with any third-party GIS software.
GIS was a very specialized technology in the past, but today it has also received a valuable application in IT. This trend is notable because GIS software vendors also offer web-based tools. Moreover, being an expert is unnecessary to take advantage of this technology.
Large companies also offer to perform complex spatial queries on corporate data through a web interface, combining them with third-party data resources. GIS is becoming more widespread, and the number of business users is expected to increase in the coming years.
GIS also plays a vital role in developing intelligent cities as data drive management. This technology allows you to exchange information in real time and visualize information from various sources, including IoT sensors.
The resulting data is used to track service delivery performance in cities and identify areas where they need improvement. In addition, geo-referenced IoT sensor data analytics help local governments develop smart solutions for cities and improve their safety.
GIS supports various analytics capabilities and is a reliable information retrieval system that helps improve management and streamline workflows. This technology provides various essential benefits, including time and cost savings. The global market for geospatial systems is growing due to the increasing demand for GIS and solutions that combine it with other technologies, including artificial intelligence.