ICIAP '99 Proceedings of the 10th International Conference on Image Analysis and Processing
Extraction of If-Then Rules from Trained Neural Network and Its Application to Earthquake Prediction
ICCI '04 Proceedings of the Third IEEE International Conference on Cognitive Informatics
Refined statistical static timing analysis through
Proceedings of the 43rd annual Design Automation Conference
Cellular automata for simulating land use changes based on support vector machines
Computers & Geosciences
The Segmentation of Skin Cancer Image Based on Genetic Neural Network
CSIE '09 Proceedings of the 2009 WRI World Congress on Computer Science and Information Engineering - Volume 05
Evaluating Urban Expansion of Nanjing City Based on Remote Sensing and GIS
ESIAT '09 Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03
ESIAT '09 Proceedings of the 2009 International Conference on Environmental Science and Information Application Technology - Volume 03
Mineral Potential Prediction Using Hybrid Intelligent Approach
ICECS '09 Proceedings of the 2009 Second International Conference on Environmental and Computer Science
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Spatial analysis is widely used in scientific research especially in the field of statistics, image processing and geoinformatics. In geoinformatics, particularly on urban growth modeling, spatial analysis is mostly performed using statistical and mathematical techniques. Recently, an intelligent approach has been introduced to model the urban growth that features dynamic behavior. Artificial Neural Network (ANN) has the capability to learn dynamic behavior and performs prediction based on its learning process. In this paper, we present an intelligent computerized tool, called DIGMAP-Detector. This tool is able to learn a pattern of urban growth based on at least two digital maps (with 4-bit/pixel bitmaps or 8-bit/pixel bitmap in Bitmap File Format (BMP)). Implemented using Java programming language, the tool reads digital map files with the size of 847 length and 474 width. Classification on the map with two independent binary classes (value 1 for urban and 0 for rural) must be prepared beforehand. By applying a cellular automata theory that considers the affect on a center pixel is influenced by its surrounding pixels (eight pixels), the tool uses a back propagation neural network to read the values of the surrounding pixels as its input layer nodes and the center pixel value as the output node. Several analyses are performed to determine the appropriate values for the neural network configuration before its learning engine starts to learn the pattern of the dynamic urban changes. When the neural network engine has learnt the pattern, prediction can be carried out to predict the missing years and future urban growth. With good prediction accuracy, urban planning and monitoring can be performed with maintaining good ecological and environmental system. In addition, better planning also contributes to economical values.