Satellite Imagery Based Population Estimation Using Geographically Weighted Poisson Regression

2017 ◽  
Vol 23 (3) ◽  
pp. 586-600
Author(s):  
Byong-Woon Jun
Keyword(s):  
Satellite Imagery ◽  
Poisson Regression ◽  
Population Estimation

scholarly journals Population Estimation Mining Using Satellite Imagery

2013 ◽  
pp. 285-296 ◽  
Author(s):  
Kwankamon Dittakan ◽  
Frans Coenen ◽  
Rob Christley ◽  
Maya Wardeh
Keyword(s):  
Satellite Imagery ◽  
Population Estimation

LambdaNet: A Fully Convolutional Architecture for Directional Change Detection

2020 ◽  
Vol 2020 (8) ◽  
pp. 114-1-114-7
Author(s):  
Bryan Blakeslee ◽  
Andreas Savakis
Keyword(s):  
Change Detection ◽  
Satellite Imagery ◽  
Classification Scheme ◽  
Deep Architecture ◽  
Binary Process ◽  
Directional Change ◽  
Class Labels ◽  
Image Pairs

Change detection in image pairs has traditionally been a binary process, reporting either “Change” or “No Change.” In this paper, we present LambdaNet, a novel deep architecture for performing pixel-level directional change detection based on a four class classification scheme. LambdaNet successfully incorporates the notion of “directional change” and identifies differences between two images as “Additive Change” when a new object appears, “Subtractive Change” when an object is removed, “Exchange” when different objects are present in the same location, and “No Change.” To obtain pixel annotated change maps for training, we generated directional change class labels for the Change Detection 2014 dataset. Our tests illustrate that LambdaNet would be suitable for situations where the type of change is unstructured, such as change detection scenarios in satellite imagery.

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