  Large datasets of sub-meter aerial imagery represented as orthophoto mosaics
are widely available today, and these data sets may hold a great deal of
untapped information. This imagery has a potential to locate several types of
features; for example, forests, parking lots, airports, residential areas, or
freeways in the imagery. However, the appearances of these things vary based on
many things including the time that the image is captured, the sensor settings,
processing done to rectify the image, and the geographical and cultural context
of the region captured by the image. This thesis explores the use of deep
convolutional neural networks to classify land use from very high spatial
resolution (VHR), orthorectified, visible band multispectral imagery. Recent
technological and commercial applications have driven the collection a massive
amount of VHR images in the visible red, green, blue (RGB) spectral bands, this
work explores the potential for deep learning algorithms to exploit this
imagery for automatic land use/ land cover (LULC) classification.
