How can GIS and AI improve land cover classification from satellite imagery?

By using AI to analyze satellite imagery in real-time, it is possible to monitor land cover changes as they occur with Fortran and Object Oriented programming . This can be especially useful for detecting changes in forest cover, urbanization, or agricultural land use. By automating many of the processes involved in land cover classification, GIS and AI can significantly reduce the cost of producing accurate land cover maps. This can make it easier for governments, non-governmental organizations, and other stakeholders to monitor and manage land use changes over time.

With Computer Vision and artificial intelligence #behavioralprogramming functionality, it is possible to automatically extract features from satellite imagery, such as vegetation, water bodies, and urban areas. This can significantly reduce the time and effort required for manual feature extraction. AI algorithms can be trained to recognize patterns in satellite imagery that are difficult for humans to discern. This can lead to improved classification accuracy, especially for complex land cover types such as mixed forests or urban areas and ancillary data, such as elevation data or soil type maps, into land cover classifications. AI algorithms can then be used to analyze the combined data and improve classification accuracy.

AI is already great for image classification for things you might take a picture of. Imagine you have a picture of a dog and a person in a room. AI can detect that there is a dog, a person, and things in the background like furniture or books. Previously you could do this using a third-party service which has a lot of work put into it to help it perform. Or you can use open-source tools to build it yourself which requires some specialized knowledge. But tools like segment-anything have made this far easier.

Climate change has a direct impact on changes in land cover. The utilization of AI imagery allows efficient and cost effective monitoring techniques to be applied to land cover assessments. This technology is useful for assessing overall environmental management and climate change impact. Challenges facing researchers and industry regarding the classification of land use include funding, the consistency and correct application of processes and methods, and lack of availability of experienced human samplers and data analyzers.

As GIS and AI technologies become more intertwined, it's important to understand the potential limitations of AI. One significant challenge is the lack of transparency in decision-making, commonly known as the "black box" issue. This is particularly important for GIS, which can directly impact people's lives. Moreover, current AI technology can produce confident responses without substantiating evidence, leading to "hallucinations." Practitioners must carefully examine assumptions and reasoning to ensure responsible use of AI in GIS. Addressing these concerns is crucial for successful and ethical integration of GIS and AI technologies. Despite these limitations, the future of GIS and AI integration is promising.

The speed at which AI imagery can correctly classify land cover and produce data regarding land change due to climate impacts like sea level rise and erosion, forest loss, ecological succession, urban and agricultural expansion, and more allows information to be captured more frequently and efficiently for trend analysis and results in better informed decisions on resource management and urban planning.

This information can also be utilized by conservationists to determine optimal species habitat ranges or habitats of concern, mitigate agricultural cultivation in areas prone to erosion to conserve topsoil, provide data for climate resiliency plans addressing sea level rise in coastal communities, or to promote the expansion of renewable energy on suitable land areas.

Incorporating GIS and AI in land cover classification significantly enhances analysis. My GIS tool uses ESA Sentinel-2 imagery to create a global land use/land cover (LULC) map at 10m resolution, derived from a deep learning model trained on over 5 billion hand-labeled pixels. With classes like water, trees, grass, crops, and built areas, this tool can aid conservation planning, food security, and hydrologic modeling. By integrating AI, the tool efficiently processes large datasets while improving classification accuracy. The model's versatility allows it to utilize multiple Sentinel-2 bands, providing more reliable and detailed LULC maps for various applications, from disaster management to urban planning. ➡️https://arcg.is/0C185S