Sökning: "Aerial Imagery"
Visar resultat 1 - 5 av 22 uppsatser innehållade orden Aerial Imagery.
- Master-uppsats, Lunds universitet/Institutionen för astronomi och teoretisk fysik
Sammanfattning : In recent years, Deep Learning has proven to be an outstanding tool in the field of computer vision showing promising results in different fields such as the analysis of medical images, obstacle detection for self-driving cars, automatic image caption generation, etc. In the case of Archaeology, the adoption of these methods in the detection of archaeological structures from aerial images has been slower than in other fields. LÄS MER
2. Comparing machine learning methods for classification and generation of footprints of buildings from aerial imageryMaster-uppsats, Blekinge Tekniska Högskola/Institutionen för programvaruteknik
Sammanfattning : The up to date mapping data is of great importance in social services and disaster relief as well as in city planning. The vast amounts of data and the constant increase of geographical changes lead to large loads of continuous manual analysis. LÄS MER
3. Classification of post-wildfire aerial imagery using convolutional neural networks : A study of machine learning and resampling techniques to assist post-wildfire effortsKandidat-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)
Sammanfattning : Assessment of post-wildfire damages to human structures is a manual task which currently uses ground-level observations of the structures by a human inspector to classify the burn severity of the affected structure. This study investigated the potential of using machine learning and specifically computer vision techniques in order to produce a classification of burn severity using post-wildfire aerial imagery. LÄS MER
- Master-uppsats, Lunds universitet/Matematik LTH
Sammanfattning : In this thesis, the viability of using Convolutional Neural Networks to detect parking spaces using aerial imagery has been evaluated. Three state of the art networks have been tested - YOLOv3, RetinaNet, and Mask R-CNN. LÄS MER
- Master-uppsats, KTH/Geoinformatik
Sammanfattning : Due to the changing climate and inherent atypically occurring meteorological events in the Arctic regions, more accurate snow quality predictions are needed in order to support the Sámi reindeer herding communities in northern Sweden that struggle to adapt to the rapidly changing Arctic climate. Spatial snow depth distribution is a crucial parameter not only to assess snow quality but also for multiple environmental research and social land use purposes. LÄS MER