New applications for mathematical morphology in urban feature extraction from high-resolution satellite imagery

2004 ◽  
Author(s):  
Xiaoying Jin ◽  
Curt H. Davis
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Mathematical Morphology ◽  
Satellite Imagery ◽  
High Resolution Satellite Imagery ◽  
New Applications

scholarly journals BUILDING FOOTPRINT EXTRACTION FROM HIGH RESOLUTION SATELLITE IMAGERY USING SEGMENTATION

2018 ◽  
Vol XLII-5 ◽  
pp. 123-128 ◽  
Author(s):  
S. Khatriker ◽  
M. Kumar
Keyword(s):  
Feature Extraction ◽  
High Resolution ◽  
Satellite Imagery ◽  
Smart Cities ◽  
Building Extraction ◽  
Rule Based ◽  
Segmented Image ◽  
High Resolution Imagery ◽  
Building Footprint ◽  
High Resolution Satellite Imagery

<p><strong>Abstract.</strong> Identification and mapping of urban features such as buildings and roads are an important task for cartographers and urban planners. High resolution satellite imagery supports the efficient extraction of manmade objects. For the planning and designing of Smart cities, building footprint information is an essential component, and geospatial technologies helps in creating this large mass of data inputs for designing and planning of smart cities. In this study segmentation approach is followed for building extraction. For extraction of buildings especially from the high resolution imagery, number of various semiautomatic and automatic methods have been developed till date to reduce the time and efforts required in manual building mapping. In this study, two semiautomatic image segmentation techniques are used for building extraction from high resolution imagery using algorithms- Multi-resolution segmentation and Rule based feature extraction, which are applied on Worldview 2 (2010) imagery of Dehradun area. The segmented image were further classified to extract buildings from the segmented image features. The study identify the usefulness of both the methods in building extraction and finds the optimum set of rules for extracting buildings from high resolution data sets. The True Positive Rate using Rule based feature extraction is 88.11<span class="thinspace"></span>% compared to 85.46<span class="thinspace"></span>% from Multi-resolution segmentation algorithm. The False Negative Rate (FNR) of Multi-resolution segmentation algorithm (16.5<span class="thinspace"></span>%.) is very less compared to Rule based feature extraction (67.5<span class="thinspace"></span>%). In the study the buildings were extracted with the accuracy of 88.9<span class="thinspace"></span>%.</p>

Evaluation of automated feature extraction algorithms using high-resolution satellite imagery across a rural-urban gradient in two unique cities in developing countries

2021 ◽  
Author(s):  
Andrew Griffin ◽  
Sean Griffin ◽  
Kristofer Lasko ◽  
Megan Maloney ◽  
S. Blundell ◽  
...  
Keyword(s):  
Developing Countries ◽  
Feature Extraction ◽  
High Resolution ◽  
Satellite Imagery ◽  
Reference Data ◽  
Accuracy Assessment ◽  
Network Connectivity ◽  
Automated Feature Extraction ◽  
Before And After ◽  
High Resolution Satellite Imagery

Feature extraction algorithms are routinely leveraged to extract building footprints and road networks into vector format. When used in conjunction with high resolution remotely sensed imagery, machine learning enables the automation of such feature extraction workflows. However, many of the feature extraction algorithms currently available have not been thoroughly evaluated in a scientific manner within complex terrain such as the cities of developing countries. This report details the performance of three automated feature extraction (AFE) datasets: Ecopia, Tier 1, and Tier 2, at extracting building footprints and roads from high resolution satellite imagery as compared to manual digitization of the same areas. To avoid environmental bias, this assessment was done in two different regions of the world: Maracay, Venezuela and Niamey, Niger. High, medium, and low urban density sites are compared between regions. We quantify the accuracy of the data and time needed to correct the three AFE datasets against hand digitized reference data across ninety tiles in each city, selected by stratified random sampling. Within each tile, the reference data was compared against the three AFE datasets, both before and after analyst editing, using the accuracy assessment metrics of Intersection over Union and F1 Score for buildings and roads, as well as Average Path Length Similarity (APLS) to measure road network connectivity. It was found that of the three AFE tested, the Ecopia data most frequently outperformed the other AFE in accuracy and reduced the time needed for editing.

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.

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