2017
DOI: 10.3390/su10010010
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Analyzing Land Cover Change and Urban Growth Trajectories of the Mega-Urban Region of Dhaka Using Remotely Sensed Data and an Ensemble Classifier

Abstract: Accurate information on, and human interpretation of, urban land cover using satellite-derived sensor imagery is critical given the intricate nature and niches of socioeconomic, demographic, and environmental factors occurring at multiple temporal and spatial scales. Detailed knowledge of urban land and their changing pattern over time periods associated with ecological risk is, however, required for the best use of critical land and its environmental resources. Interest in this topic has increased recently, d… Show more

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Cited by 56 publications
(54 citation statements)
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References 45 publications
(41 reference statements)
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“…This chapter is divided into 2 parts: data description and experimental results. To demonstrate completely the performance of WindNet proposed in this paper, this chapter will also include comparisons of very popular and commonly used machine learning algorithms, such as support vector machine (SVM) [33][34][35][36][37][38], random forest (RF) [39][40][41][42][43][44], decision tree (DT) [45][46][47][48][49][50] and MLP.…”
Section: Resultsmentioning
confidence: 99%
“…This chapter is divided into 2 parts: data description and experimental results. To demonstrate completely the performance of WindNet proposed in this paper, this chapter will also include comparisons of very popular and commonly used machine learning algorithms, such as support vector machine (SVM) [33][34][35][36][37][38], random forest (RF) [39][40][41][42][43][44], decision tree (DT) [45][46][47][48][49][50] and MLP.…”
Section: Resultsmentioning
confidence: 99%
“…LANDSAT ACCURACY ASSESSMENT Random sampling method was adopted in this study to collect the samples for validation (Hassan et al 2017). In this case, 50 samples for each LULC class were generated based on the rule of thumb of Congalton and Green's using Google Earth imagery.…”
Section: Resultsmentioning
confidence: 99%
“…In order to fully demonstrate the performance of the EPNet proposed in this paper, this chapter includes comparisons between Support Vector Machine (SVM) [25][26][27][28][29][30], Random Forest (RF) [31][32][33][34][35][36], Decision Tree (DT) [37][38][39][40][41][42], MLP, CNN and LSTM. Figure 6 is the Electric Power Markets (PJM) Regulation Zone Preliminary Billing Data [43] used in this experiment, this data records the regulation market capacity clearing price of every half hour in 2017.…”
Section: Resultsmentioning
confidence: 99%