Semantic Segmentation of High Resolution Satellite Imagery using Generative Adversarial Networks with Progressive Growing

2021 ◽  
Vol 12 (5) ◽  
pp. 439-448
Author(s):  
Edward Collier ◽  
Supratik Mukhopadhyay ◽  
Kate Duffy ◽  
Sangram Ganguly ◽  
Geri Madanguit ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Semantic Segmentation ◽  
Generative Adversarial Networks ◽  
Adversarial Networks ◽  
High Resolution Satellite Imagery

scholarly journals Identifying Urban Poverty Using High-Resolution Satellite Imagery and Machine Learning Approaches: Implications for Housing Inequality

Land ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 648
Author(s):  
Guie Li ◽  
Zhongliang Cai ◽  
Yun Qian ◽  
Fei Chen
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Satellite Imagery ◽  
Low Income ◽  
Urban Poverty ◽  
Image Features ◽  
Housing Affordability ◽  
Learning Approaches ◽  
Housing Inequality ◽  
High Resolution Satellite Imagery

Enriching Asian perspectives on the rapid identification of urban poverty and its implications for housing inequality, this paper contributes empirical evidence about the utility of image features derived from high-resolution satellite imagery and machine learning approaches for identifying urban poverty in China at the community level. For the case of the Jiangxia District and Huangpi District of Wuhan, image features, including perimeter, line segment detector (LSD), Hough transform, gray-level cooccurrence matrix (GLCM), histogram of oriented gradients (HoG), and local binary patterns (LBP), are calculated, and four machine learning approaches and 25 variables are applied to identify urban poverty and relatively important variables. The results show that image features and machine learning approaches can be used to identify urban poverty with the best model performance with a coefficient of determination, R2, of 0.5341 and 0.5324 for Jiangxia and Huangpi, respectively, although some differences exist among the approaches and study areas. The importance of each variable differs for each approach and study area; however, the relatively important variables are similar. In particular, four variables achieved relatively satisfactory prediction results for all models and presented obvious differences in varying communities with different poverty levels. Housing inequality within low-income neighborhoods, which is a response to gaps in wealth, income, and housing affordability among social groups, is an important manifestation of urban poverty. Policy makers can implement these findings to rapidly identify urban poverty, and the findings have potential applications for addressing housing inequality and proving the rationality of urban planning for building a sustainable society.

scholarly journals Lake-to-Lake Cloud Bands: Frequencies and Locations

2007 ◽  
Vol 135 (12) ◽  
pp. 4202-4213 ◽  
Author(s):  
Yarice Rodriguez ◽  
David A. R. Kristovich ◽  
Mark R. Hjelmfelt
Keyword(s):  
High Resolution ◽  
Great Lakes ◽  
Satellite Imagery ◽  
Lake Superior ◽  
Great Lakes Region ◽  
Lake Effect ◽  
Time Period ◽  
High Resolution Satellite Imagery

Abstract Premodification of the atmosphere by upwind lakes is known to influence lake-effect snowstorm intensity and locations over downwind lakes. This study highlights perhaps the most visible manifestation of the link between convection over two or more of the Great Lakes lake-to-lake (L2L) cloud bands. Emphasis is placed on L2L cloud bands observed in high-resolution satellite imagery on 2 December 2003. These L2L cloud bands developed over Lake Superior and were modified as they passed over Lakes Michigan and Erie and intervening land areas. This event is put into a longer-term context through documentation of the frequency with which lake-effect and, particularly, L2L cloud bands occurred over a 5-yr time period over different areas of the Great Lakes region.

scholarly journals A fuzzy decision making system for building damage map creation using high resolution satellite imagery

2013 ◽  
Vol 13 (2) ◽  
pp. 455-472 ◽  
Author(s):  
H. Rastiveis ◽  
F. Samadzadegan ◽  
P. Reinartz
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Damage Assessment ◽  
Fuzzy Inference ◽  
Earthquake Damage ◽  
Fuzzy Decision Making ◽  
Inference System ◽  
High Resolution Satellite Imagery ◽  
Roof Part ◽  
Earthquake Damage Assessment

Abstract. Recent studies have shown high resolution satellite imagery to be a powerful data source for post-earthquake damage assessment of buildings. Manual interpretation of these images, while being a reliable method for finding damaged buildings, is a subjective and time-consuming endeavor, rendering it unviable at times of emergency. The present research, proposes a new state-of-the-art method for automatic damage assessment of buildings using high resolution satellite imagery. In this method, at the first step a set of pre-processing algorithms are performed on the images. Then, extracting a candidate building from both pre- and post-event images, the intact roof part after an earthquake is found. Afterwards, by considering the shape and other structural properties of this roof part with its pre-event condition in a fuzzy inference system, the rate of damage for each candidate building is estimated. The results obtained from evaluation of this algorithm using QuickBird images of the December 2003 Bam, Iran, earthquake prove the ability of this method for post-earthquake damage assessment of buildings.

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