Abstracts-The study of urban poverty using high resolution satellite imagery and environmental data set has been widely used in many countries since early year 2000 to estimate, detect and identify the poor areas. By using remote sensing approach the identification of poor area can be pre-predicted without intensively to go to the ground. Due to high life expenses at urban area in Malaysia recently, the investigation of urban poverty in dense areas becomes much important especially in identifying the squatter and low cost houses which are expected to be settlement for people who are at low level income. This paper is going to discuss approaches being used in our pilot study to determine urban poverty areas which identified by correlation studies on high resolution IKONOS satellite imagery. The expected end result will be verified through multiple analyses via Remote Sensing and statistical approach.

Accuracy assessment for map comparison is commonly found in urban planning research, especially for detecting error in remotely sensed imagery data. It is to compare two sources of spatial information. In analyzing such information quantitatively, the two datasets are summarized in a confusion matrix, which is represented in a form of percentage of predicted value against its actual data (ground truth). The common acceptable percentage is eighty percent and above. In this paper, we present a new way of accuracy assessment by introducing an additional value called residual error (or predicted error). The residual error is the percentage of error exists when two sources of major errors called mis-classification and mis-location are integrated. Such residual error is incorporated into the assessment so that the results are more accurate and comprehensive. As a case study, we calculate the residual errors of five independent image classifications from six different datasets. Therefore, the accuracy assessment is performed with more details that include not only the confusion matrix, but also the residual errors. In this way, the results of the change detection process can help in doing further analysis for urban growth and land development, particularly for town area.

No abstract

No abstract