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CLASSIFICATION OF URBAN AERIAL DATA BASED ON PIXEL LABELLING WITH DEEP CONVOLUTIONAL NEURAL NETWORKS AND LOGISTIC REGRESSION
aerial data urban areas evidence combination object classification deep feature learning
2016/11/23
The recent success of deep convolutional neural networks (CNN) on a large number of applications can be attributed to large amounts of available training data and increasing computing power. In this p...
A COMPARISON OF BAYESIAN AND EVIDENCE-BASED FUSION METHODS FOR AUTOMATED BUILDING DETECTION IN AERIAL DATA
Fusion Building detection Automation Aerial image Laser scanning
2015/12/31
Automated approaches to building detection are of great importance in a number of different applications including map updating and monitoring of informal settlements. With the availability of multi-s...
MAPPING CAPABILITY OF A LOW-COST AERIAL DATA ACQUISITION PLATFORM–FIRST RESULTS
low-cost platform GPS IMU aerial data acquisition building extraction surface mapping
2015/9/17
Based on a first prototype, an improved low-cost aerial platform was developed at the Institute of Digital Image Processing in order to be used for immediate acquisition and processing of image data w...
AUTOMATIC EXTRACTION OF URBAN OBJECTS FROM MULTI-SOURCE AERIAL DATA
LiDAR buildings road extraction automated classifi cation city models
2016/3/1
Today, one of the main applications of multi-source aerial data is the city modelling. The capability to automatically detect objects of
interest starting from LiDAR and multi-spectral data is a comp...
COMPARATIVE ANALYSIS OF AUTOMATIC APPROACHES TO BUILDING DETECTION FROM MULTI-SOURCE AERIAL DATA
Building detection comparative analysis pixel-based and object-based algorithms
2015/11/16
Automatic building detection has been a hot topic since the early 1990’s. Early approaches were based on a single aerial image.
Detecting buildings is a difficult task so it can be more effective wh...