scholarly journals US EPA EnviroAtlas Meter-Scale Urban Land Cover (MULC): 1-m Pixel Land Cover Class Definitions and Guidance

2020 ◽  
Vol 12 (12) ◽  
pp. 1909 ◽  
Author(s):  
Andrew Pilant ◽  
Keith Endres ◽  
Daniel Rosenbaum ◽  
Gillian Gundersen
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Environmental Protection Agency ◽  
The United States ◽  
Urban Land ◽  
Environmental Data ◽  
Tree Planting ◽  
Goods And Services ◽  
Urban Land Cover ◽  
Us Epa

This article defines the land cover classes used in Meter-Scale Urban Land Cover (MULC), a unique, high resolution (one meter2 per pixel) land cover dataset developed for 30 US communities for the United States Environmental Protection Agency (US EPA) EnviroAtlas. MULC data categorize the landscape into these land cover classes: impervious surface, tree, grass-herbaceous, shrub, soil-barren, water, wetland and agriculture. MULC data are used to calculate approximately 100 EnviroAtlas metrics that serve as indicators of nature’s benefits (ecosystem goods and services). MULC, a dataset for which development is ongoing, is produced by multiple classification methods using aerial photo and LiDAR datasets. The mean overall fuzzy accuracy across the EnviroAtlas communities is 88% and mean Kappa coefficient is 0.84. MULC is available in EnviroAtlas via web browser, web map service (WMS) in the user’s geographic information system (GIS), and as downloadable data at EPA Environmental Data Gateway. Fact sheets and metadata for each MULC community are available through EnviroAtlas. Some MULC applications include mapping green and grey infrastructure, connecting land cover with socioeconomic/demographic variables, street tree planting, urban heat island analysis, mosquito habitat risk mapping and bikeway planning. This article provides practical guidance for using MULC effectively and developing similar high resolution (HR) land cover data.

scholarly journals Urban Land Cover Classification of High-Resolution Aerial Imagery Using a Relation-Enhanced Multiscale Convolutional Network

2020 ◽  
Vol 12 (2) ◽  
pp. 311 ◽  
Author(s):  
Chun Liu ◽  
Doudou Zeng ◽  
Hangbin Wu ◽  
Yin Wang ◽  
Shoujun Jia ◽  
...  
Keyword(s):  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Urban Land ◽  
Small Sample ◽  
Connectivity Pattern ◽  
Feature Maps ◽  
Convolutional Network ◽  
Urban Land Cover

Urban land cover classification for high-resolution images is a fundamental yet challenging task in remote sensing image analysis. Recently, deep learning techniques have achieved outstanding performance in high-resolution image classification, especially the methods based on deep convolutional neural networks (DCNNs). However, the traditional CNNs using convolution operations with local receptive fields are not sufficient to model global contextual relations between objects. In addition, multiscale objects and the relatively small sample size in remote sensing have also limited classification accuracy. In this paper, a relation-enhanced multiscale convolutional network (REMSNet) method is proposed to overcome these weaknesses. A dense connectivity pattern and parallel multi-kernel convolution are combined to build a lightweight and varied receptive field sizes model. Then, the spatial relation-enhanced block and the channel relation-enhanced block are introduced into the network. They can adaptively learn global contextual relations between any two positions or feature maps to enhance feature representations. Moreover, we design a parallel multi-kernel deconvolution module and spatial path to further aggregate different scales information. The proposed network is used for urban land cover classification against two datasets: the ISPRS 2D semantic labelling contest of Vaihingen and an area of Shanghai of about 143 km2. The results demonstrate that the proposed method can effectively capture long-range dependencies and improve the accuracy of land cover classification. Our model obtains an overall accuracy (OA) of 90.46% and a mean intersection-over-union (mIoU) of 0.8073 for Vaihingen and an OA of 88.55% and a mIoU of 0.7394 for Shanghai.

Recurrent Shadow Attention Model (RSAM) for shadow removal in high-resolution urban land-cover mapping

2020 ◽  
Vol 247 ◽  
pp. 111945 ◽  
Author(s):  
Yindan Zhang ◽  
Gang Chen ◽  
Jelena Vukomanovic ◽  
Kunwar K. Singh ◽  
Yong Liu ◽  
...  
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Urban Land ◽  
Land Cover Mapping ◽  
Shadow Removal ◽  
Attention Model ◽  
Urban Land Cover

scholarly journals WorldView-2 Data for Hierarchical Object-Based Urban Land Cover Classification in Kigali: Integrating Rule-Based Approach with Urban Density and Greenness Indices

2019 ◽  
Vol 11 (18) ◽  
pp. 2128 ◽  
Author(s):  
Mugiraneza ◽  
Nascetti ◽  
Ban
Keyword(s):  
High Resolution ◽  
Land Cover ◽  
Informal Settlements ◽  
Urban Land ◽  
Urban Environments ◽  
Urban Density ◽  
Rule Based ◽  
Urban Land Cover ◽  
Object Based ◽  
Rule Based Approach

The emergence of high-resolution satellite data, such as WorldView-2, has opened the opportunity for urban land cover mapping at fine resolution. However, it is not straightforward to map detailed urban land cover and to detect urban deprived areas, such as informal settlements, in complex urban environments based merely on high-resolution spectral features. Thus, approaches integrating hierarchical segmentation and rule-based classification strategies can play a crucial role in producing high quality urban land cover maps. This study aims to evaluate the potential of WorldView-2 high-resolution multispectral and panchromatic imagery for detailed urban land cover classification in Kigali, Rwanda, a complex urban area characterized by a subtropical highland climate. A multi-stage object-based classification was performed using support vector machines (SVM) and a rule-based approach to derive 12 land cover classes with the input of WorldView-2 spectral bands, spectral indices, gray level co-occurrence matrix (GLCM) texture measures and a digital terrain model (DTM). In the initial classification, confusion existed among the informal settlements, the high- and low-density built-up areas, as well as between the upland and lowland agriculture. To improve the classification accuracy, a framework based on a geometric ruleset and two newly defined indices (urban density and greenness density indices) were developed. The novel framework resulted in an overall classification accuracy at 85.36% with a kappa coefficient at 0.82. The confusion between high- and low-density built-up areas significantly decreased, while informal settlements were successfully extracted with the producer and user’s accuracies at 77% and 90% respectively. It was revealed that the integration of an object-based SVM classification of WorldView-2 feature sets and DTM with the geometric ruleset and urban density and greenness indices resulted in better class separability, thus higher classification accuracies in complex urban environments.

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