Computer Vision and Remote Sensing

Computer Vision (CV), deep learning, and artificial intelligence (AI) methods for applications in remote sensing can support and resolve challenges for large satellite image data sets by utilizing high-performance-based models to collect and identify features in an environment with precise accuracy and speed. CV and deep learning can help the end-user to understand the data and improve the overall accuracy of the feature classification and can enhance the reliability, and assess features of image data collected. Computer Vision’s algorithms can remove noise and enhance satellite and aerial image data that can improve the analysis of large areas to classify objects, and features, detect land change, data fusion, cloud removal, and spectral analysis from satellite or aerial imagery that will overcome challenges of remote sensing data.

Images collected by satellites or unmanned aerial vehicles (UAV) by applying CV, AI (Artificial Intelligence), ML (Machine Learning), and deep learning methods, these models can provide near real-time reports for areas with complex feature distribution such as in the applications of mineral mapping, agriculture, climate change, environmental monitoring and management, and wildlife conservation.

WorldView-4 Satellite Image of Agriculture Fields – Xinjiang Ghulja, China (30cm)

CV for Agriculture Mapping

Utilizing high-resolution, multispectral satellite images, and Computer Vision (CV) algorithms, image data is collected and transferred into management solutions for crop health and improved production targets. CV and geographic information systems (GIS) tools can help farmers to conduct crop forecasting and manage their agriculture production by utilizing image data collected by satellites, fix wing aircraft, or unmanned aerial vehicles (UAV). This data is collected and processed to provide a standard NDVI vegetation index or more complex vegetation and soil algorithms, to identify crop stress, and production yields, and manage tree grading for large agriculture yields. The ability of CV and GIS to analyze and visualize agricultural environments and workflows has proven to be very beneficial to those involved in the farming industry.