D06

Research Abstracts on Spatial Information ScienceCSIS DAYS 2015

Remote Sensing Identification of Buildings based on Machine Learning
Zhiling Guo, Xiaowei Shao, Yongwei Xu and Ryosuke Shibasaki
Center for Spatial Information Science, The University of Tokyo
Email: <guozhilingty@gmail.com>(1) Introduction:Withtheacceleration
ofurbanization process, maps used to illustratebuildings and their distributions are quite
significant. However, under the circumstances ofsome remote and rural areas, such kinds of maps
are often insufficient and defective. This study aims
to investigate the new way based on machinelearning for building identification.
(2) Data: The data used in this study are captured from
Google satellite map in February, 2015, indicating
the area called Kaysone, which locates in theSavannakhet province of Laos.
(3) Methodology: In order to identify the buildings, andcompare with the former approaches, our approach
has some predominant unique points. We utilize themachine learning methodologies based on Adaboost
and Convolutional Neural Networks (CNN)
respectively, both contain two steps. In training step,
the input is the training data which contains theinformation of different feature, after trained by thealgorithm, the classifiers would be generated; intesting step, the input is the testing data, theidentification result would be generated via
classifiers. However, the information of the featureused in both algorithms is different. AdaBoost
method utilized in the experiment mainly based on
Color Feature and Haar-like Feature could identify
the buildings via the color and texture information ofbuildings; CNN method could transform complex
and non-linear mapping from high-dimensionalfeature space to a low dimensional space whereclassification is obvious. The CNN method could
express deeper information of the buildings with thehelp of convolution kernel etc. Due to the principleof CNN, allows it to be successfully applied to suchkind of field.
(4) Results: For different study areas, the overall
accuracy of our methods could be up to about 95%,
and the Kappa Coefficient could be up to 0.824,
which could show a high performance. It means, themajority of the buildings could be identified, such as
the result in Figure 1. In addition, we could infer theperformance of the proposed method is quite stablefor different areas.
(5) Future steps: In order to test the stability of themethod, more areas including diverse characteristics
need to be tested. In addition, by observing theidentification results, some identification target could
not be extracted due to the different color andcharacteristics from the training samples. We
consider solving such kind of problems by utilizing
transfer learning method, which could enhance theclassifier, and make the classifier obtain the ability ofidentifying the new targets that are similar to theformer targets.

Figure 1: The identification result of the method. The data (left) used in this study is captured
from Google satellite map, and the right image is the identification result. Where Gray refers to
the unknown part; Green means the actual buildings that were correctly classified as buildings;
Blue indicates the non-buildings were incorrectly labeled as buildings; Red shows the buildingsthat were incorrectly marked as non-buildings, and Black means all the things correctly classified
as non-buildings.

- 56 -