Satellite and Aerial Imagery Analysis
Thanks to improved hardware and deep learning libraries, classification of satellite and aerial imagery can now be performed at astonishing speeds and at a much lower cost that was previously possible. Below we briefly describe the steps required for the classification of buildings using satellite imagery.
This is made up of the following steps:
1 Building footprint detection. This is done using NVIDA’s DIGITS library (included in the Deep Learning Box Software Stack) which has shown to detect more than 90% of the large buildings such as commercial buildings. In regions where building boundaries are close together the detection of individual buildings can drop down to 70% or result in building not being clearly demarcated. However, depending on the region, detection thresholds may be modified to achieve better than 70% accuracies. See images below for examples of classified images:
2 Classification of buildings. This is done by training a model based on pre-classified data. Through sources such as OpenStreetMap and Local Government datasets we can obtain some pre-classified buildings for the study area. These are used to create classification models that can then be applied to new building footprints. The classification algorithms usually fall within an accuracy range of 95-70%. See image below showing pre-classified buildings.
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