Land Use and Land Cover Classification Using Deep Learning Techniques
This work addresses the problem of extracting untapped information from widely available aerial imagery for applications in environmental monitoring or urban planning, but it is incremental as it applies existing deep learning methods to a specific domain.
The paper tackles land use and land cover classification by applying deep convolutional neural networks to very high-resolution RGB aerial imagery, achieving automatic classification of features like forests and residential areas.
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.