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VillageFinder: Segmentation of
Nucleated Villages in Satellite Imagery
Kashif Murtaza, Sohaib Khan,
Nasir
Rajpoot, BMVC 2009
50
sq km image of rural area, with village outpus marked in red. 2.3% false
positives at 0.01% false negatives
[high res
version 30MB JPG]
Downloads
 BMVC'09 paper [7.5
MB PDF]
BMVC'09 one page
summary [630 KB PDF]
BMVC'09 poster [3.2 MB PDF]
Code
and Datasets(All in one)[339 MB] used in the paper
Abstract
Geo-spatial data on village locations, their
size, population and other parameters is scarcely available to decision
makers in many developing countries. In this paper, we demonstrate an
automatic “crawler” which can segment nucleated villages from satellite
imagery freely available in public domain geographic information systems
such as Google Earth. Our approach is to use frequency and color
features to generate a number of weak classifiers, which are then combined
through Adaboost to produce the final classifier. We use a total of 69
features in the generation of the weak classifiers, including phase
gradients, cornerness measures and color features. Our primary dataset
consists of 60 images having more than 345 million pixels and covering
more than 100 sq km of area, containing nucleated villages in fifteen
countries, spread over four continents and captured by different sensors.
Using six manual annotations for ground-truth, we perform five-fold cross validation, using 25% of
data for testing. Our results show an Equal Error Rate
(EER) of around 3.4%. Using the trained classifier, we detect
villages on a 50 sq. km image (close
to 184 million pixels) from a different site than
the images used in training, and demonstrate highly accurate extraction of
villages with 2.3% false positives and 0.01% false negatives.
Dataset
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Department
of Computer Science
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