How can satellite imagery and machine learning improve land use classification accuracy?

From my experience satellite imagery can replace the need for air photos and AI could possibly help with interpretation of vegetation cover types, landscape topography, and by inference drought conditions or saturation. Imagery with respect to determining soil types for land use classification is limited in its capability and must have ground truthing as per standard field soil survey methods. The properties examined during field soil surveying and the integrated effects of these properties cannot be determined remotely at this time to my knowledge.

Combining satellite imagery with machine learning creates a robust framework for land use classification. By harnessing these advanced technologies, researchers and policymakers can access precise and current data about the Earth’s surface, facilitating informed decisions for sustainable land management and environmental protection.

I believe machine learning has great potential for analyzing satellite images. Supervised learning allows us to categorize land as cities, farmland, or forests, while unsupervised learning uncovers new patterns in land use. This creates detailed classifications that adapt to changes, which is vital for effective environmental management. However, we must recognize the limitations of these technologies. Satellite images can’t replace ground truthing, especially for accurately classifying soil types, which requires on-the-ground assessments. Therefore, combining advanced technology with local expertise is essential for achieving accurate land use classifications and ensuring that our strategies are relevant to real-world conditions.

supervized machine learning can be useful for interpreting satellite imagery data for above ground factors used to classify land use (vegetation and topography). A key qualifier is that a qualified professional should be involved in teaching the machine!!

This could include standardizing data collection methods, sharing satellite imagery and machine learning techniques, and fostering partnerships between governments, NGOs, and research institutions. By enhancing classification accuracy through collective efforts, we can better monitor land use changes, assess their impacts, and develop sustainable strategies that promote ecological balance and resilience worldwide.

Satellite imagery is an invaluable tool in geology and hydrogeology. One of the more interesting applications I have used satellite imagery for is quantifying the groundwater usage of agricultural land in an effort to calculate sustainable groundwater yield. Machine learning seems like it is in the early stages of refining this process by estimating crop types, soil types and crop irrigation demand. I can see these sustainable yield calculations becoming ever more efficient and accurate to cover larger areas of agricultural land at higher resolution. That's valuable information for Groundwater Sustainability Agencies in California to know.

I have used satellite imagery as a base map on which to electronically record real time environmental changes during linear construction projects and track changes over time from start of construction to completion of land reclamation. I have seen two complete failures of the use of saltellite imagery and AI to classify soil landscapes without adequate ground truthing. In both cases the algorithms did not follow or use the existing classifications for soils in Canada but rather focussed on statistical integration of a few soil properties. The resulting classifications bore no resemblance to reality and cost the client users a lot to correct.