Information extracted from aerial photographs has found applications in a wide
range of areas including urban planning, crop and forest management, disaster
relief, and climate modeling. At present, much of the extraction is still
performed by human experts, making the process slow, costly, and error prone.
The goal of this thesis is to develop methods for automatically extracting the
locations of objects such as roads, buildings, and trees directly from aerial
images.
We investigate the use of machine learning methods trained on aligned aerial
images and possibly outdated maps for labeling the pixels of an aerial image
with semantic labels. We show how deep neural networks implemented on modern
GPUs can be used to efficiently learn highly discriminative image features. We
then introduce new loss functions for training neural networks that are
partially robust to incomplete and poorly registered target maps. Finally, we
propose two ways of improving the predictions of our system by introducing
structure into the outputs of the neural networks.
We evaluate our system on the largest and most-challenging road and building
detection datasets considered in the literature and show that it works reliably
under a wide variety of conditions. Furthermore, we are releasing the first
large-scale road and building detection datasets to the public in order to
facilitate future comparisons with other methods.
| Identifer | oai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OTU.1807/35911 |
| Date | 09 August 2013 |
| Creators | Mnih, Volodymyr |
| Contributors | Hinton, Geoffrey |
| Source Sets | Library and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada |
| Language | en_ca |
| Detected Language | English |
| Type | Thesis |
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