In this project, I utilized satellite imagery data from the Landsat series to analyze urban area environments. By combining traditional algorithms with artificial intelligence techniques, I developed a system that can process and interpret large-scale satellite images effectively.

Tensorflow, Keras, Scikit-image, GeoTiff, Pillow, OpenCV, Numpy, Pandas(geopandas), rioxarray, earthpy

By leveraging cutting-edge technologies and algorithms, this project demonstrates my expertise in satellite image analysis, machine learning, and data visualization. The developed system provides valuable insights into urban environments, aiding in urban planning, environmental monitoring, and decision-making processes.

At over 40 years, the Landsat series of satellites provides the longest temporal record of moderate resolution multispectral data of the Earth’s surface on a global basis. The Landsat record has remained remarkably unbroken, proving a unique resource to assist a broad range of specialists in managing the world’s food, water, forests, and other natural resources for a growing world population. It is a record unmatched in quality, detail, coverage, and value. Source: USGS

Landsat data are spectral and collected using a platform mounted on a satellite in space that orbits the earth. The spectral bands and associated spatial resolution of the first 9 bands in the Landsat 8 sensor are listed below.

Applying the segmentation (MaskRCNN) to multi-band images. https://www.tensorflow.org/tutorials/images/segmentation