With the advent of private space agencies and the subsequent increase in commercial satellites, high-definition satellite imagery is an increasingly commoditized resource. 

This has led to tremendous gains in the geospatial industry, where applying AI to geospatial data can facilitate map building, the analysis and audit of physical and environmental assets, and resource monitoring.

AI-assisted geospatial analysis is used to help track and measure deforestation with minimal fieldwork, map permafrost and detect illegal or changing land uses. You can read more on geospatial AI here.

Geospatial AI and Geographic Information Systems (GIS)

Geographic Information Systems (GIS) provide a powerful framework for building AI enhancements. Molding GIS and AI together can come to be known as GeoAI, where data combines with algorithms to model geographic systems in real-time. 

GeoAI seeks to advance the discovery and understanding of natural and human geographic phenomena and their dynamics. Aims include combating the climate crisis, reducing habitat destruction, tracking weather dynamics, and monitoring resources. 

AIs are being used to predict geospatial dynamics from labeled and unlabeled image and video data, including LIDAR, hyperspectral, multispectral, and thermal sensors.

Machine learning is now automating the process and analysis of complex geospatial datasets to provide a range of insights in considerably less time than a manual analysis would take. 

Data Labeling for Geospatial Analysis

Remote sensing and LIDAR technologies have greatly enriched the geospatial detail obtainable from satellites or UAVs. However, making this data available to supervised or semi-supervised models often requires data annotation. 

LIDAR annotation and other complex image annotation require high domain specialism and skill. It’s also essential to take advantage of the latest data labeling tools. 

Aya Data assists geospatial AI companies and GeoAI projects by creating high-quality labeled geospatial datasets. Our highly-skilled data labeling team can work with diverse satellite image and video data to create robust datasets for training geospatial AIs. View our Image Segmentation in Satellite Imagery case study here.

Services: Polygon, Bounding Box, Semantic Segmentation, Key Points

Domain Expertise: Geospatial Domain Experience