Automated Extraction of Visible Floodwater in Dense Urban Areas from RGB Aerial Photos

Rapid response mapping of floodwater extents in urbanized areas, while essential for early damage assessment and rescue operations, also presents significant image interpretation challenges. Images from visible band (red–green–blue (RGB)) remote sensors are the most common and cost-effective for real-time applications. Based on an understanding of the differing characteristics of turbid floodwater and urban land surface classes, a robust method was developed and automatized to extract visible floodwater using RGB band digital numbers. The methodology was applied to delineate visible floodwater distribution from very high-resolution aerial image data acquired during the 2013 Calgary flood event. The methodology development involved segment- and pixel-based feature analysis, rule development, automated feature extraction, and result validation processing. The accuracies for the visible floodwater class were above 0.8394% and the overall accuracies were above 0.9668% at both pixel and segment levels for three test sites with diverse urban landscapes.

Download Full-text

ANALYSIS OF DEBRIS FLOW BEHAVIOR USING AIRBORNE LIDAR AND IMAGE DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsarchives-xli-b8-85-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 85-88 ◽

Cited By ~ 1

Author(s):

G. Kim ◽

C. Y. Yune ◽

J. Paik ◽

S. W. Lee

Keyword(s):

Debris Flow ◽

Land Surface ◽

Flow Behavior ◽

Image Data ◽

Airborne Lidar ◽

Aerial Image ◽

Cross Sectional ◽

Erosion And Deposition ◽

Surface Models ◽

Before And After

The frequency of debris flow events caused by severe rainstorms has increased in Korea. LiDAR provides high-resolution topographical data that can represent the land surface more effectively than other methods. This study describes the analysis of geomorphologic changes using digital surface models derived from airborne LiDAR and aerial image data acquired before and after a debris flow event in the southern part of Seoul, South Korea in July 2011. During this event, 30 houses were buried, 116 houses were damaged, and 22 human casualties were reported. Longitudinal and cross-sectional profiles of the debris flow path reconstructed from digital surface models were used to analyze debris flow behaviors such as landslide initiation, transport, erosion, and deposition. LiDAR technology integrated with GIS is a very useful tool for understanding debris flow behavior.

Download Full-text

AUTOMATIC BUILDING EXTRACTION FROM LIDAR POINT CLOUD DATA IN THE FUSION OF ORTHOIMAGE

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-541-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 541-546

Author(s):

B. Hujebri ◽

M. Ebrahimikia ◽

H. Enayati

Keyword(s):

Urban Areas ◽

Point Cloud ◽

Mean Shift ◽

Three Dimensional ◽

Image Data ◽

Aerial Image ◽

Normal Vector ◽

Cloud Data ◽

Mean Shift Algorithm ◽

Research Experiment

Abstract. Three-dimensional building models are important in various applications such as disaster management and urban planning. In this paper, a method based on the fusion of LiDAR point cloud and aerial image data sources has been proposed. The first step of the proposed method is to separate ground and non-ground (that contain 3d objects like buildings, trees, …) points using cloth simulation filtering and then normalize the non-ground points. This research experiment applied a 0.1 threshold for the z component of the normal vector to remove wall points, and 2-meter height threshold to remove off-terrain objects lower than the minimum building height. It is possible to discriminate vegetation and building based on spectral information from orthoimage. After elimination of vegetation points, the mean shift algorithm applied on remaining points to detect buildings. This method provides good performance in dense urban areas with complex ground covering such as trees, shrubs, short walls, and vehicles.

Download Full-text

Comparison of Thermal Infrared-Derived Maps of Irrigated and Non-Irrigated Vegetation in Urban and Non-Urban Areas of Southern California

Remote Sensing ◽

10.3390/rs12244102 ◽

2020 ◽

Vol 12 (24) ◽

pp. 4102

Author(s):

Red Willow Coleman ◽

Natasha Stavros ◽

Glynn Hulley ◽

Nicholas Parazoo

Keyword(s):

Los Angeles ◽

Land Surface ◽

Urban Areas ◽

Southern California ◽

Hydrological Cycle ◽

Space Station ◽

Urban Landscapes ◽

Landsat 8 ◽

Arid Environments ◽

Vegetation Fraction

It is important to understand the distribution of irrigated and non-irrigated vegetation in rapidly expanding urban areas that are experiencing climate-induced changes in water availability, such as Los Angeles, California. Mapping irrigated vegetation in Los Angeles is necessary for developing sustainable water use practices and accurately accounting for the megacity’s carbon exchange and water balance changes. However, pre-existing maps of irrigated vegetation are largely limited to agricultural regions and are too coarse to resolve heterogeneous urban landscapes. Previous research suggests that irrigation has a strong cooling effect on vegetation, especially in semi-arid environments. The July 2018 launch of the ECOsystem Spaceborne Thermal Radiometer on Space Station (ECOSTRESS) offers an opportunity to test this hypothesis using retrieved land surface temperature (LST) data in complex, heterogeneous urban/non-urban environments. In this study, we leverage Landsat 8 optical imagery and 30 m sharpened afternoon summertime ECOSTRESS LST, then apply very high-resolution (0.6–10 m) vegetation fraction weighting to produce a map of irrigated and non-irrigated vegetation in Los Angeles. This classification was compared to other classifications using different combinations of sensors in order to offer a preliminary accuracy and uncertainty assessment. This approach verifies that ECOSTRESS LST data provides an accurate map (98.2% accuracy) of irrigated urban vegetation in southern California that has the potential to reduce uncertainties in regional carbon and hydrological cycle models.

Download Full-text

ANALYSIS OF DEBRIS FLOW BEHAVIOR USING AIRBORNE LIDAR AND IMAGE DATA

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xli-b8-85-2016 ◽

2016 ◽

Vol XLI-B8 ◽

pp. 85-88

Author(s):

G. Kim ◽

C. Y. Yune ◽

J. Paik ◽

S. W. Lee

Keyword(s):

Debris Flow ◽

Land Surface ◽

Flow Behavior ◽

Image Data ◽

Airborne Lidar ◽

Aerial Image ◽

Cross Sectional ◽

Erosion And Deposition ◽

Surface Models ◽

Before And After

Download Full-text

Fig Plant Segmentation from Aerial Images Using a Deep Convolutional Encoder-Decoder Network

Remote Sensing ◽

10.3390/rs11101157 ◽

2019 ◽

Vol 11 (10) ◽

pp. 1157 ◽

Cited By ~ 8

Author(s):

Jorge Fuentes-Pacheco ◽

Juan Torres-Olivares ◽

Edgar Roman-Rangel ◽

Salvador Cervantes ◽

Porfirio Juarez-Lopez ◽

...

Keyword(s):

Precision Agriculture ◽

Image Data ◽

Ground Truth ◽

Aerial Images ◽

Aerial Image ◽

Data Set ◽

Visual Appearance ◽

Aerial Robots ◽

Lighting Conditions ◽

Convolutional Encoder

Crop segmentation is an important task in Precision Agriculture, where the use of aerial robots with an on-board camera has contributed to the development of new solution alternatives. We address the problem of fig plant segmentation in top-view RGB (Red-Green-Blue) images of a crop grown under open-field difficult circumstances of complex lighting conditions and non-ideal crop maintenance practices defined by local farmers. We present a Convolutional Neural Network (CNN) with an encoder-decoder architecture that classifies each pixel as crop or non-crop using only raw colour images as input. Our approach achieves a mean accuracy of 93.85% despite the complexity of the background and a highly variable visual appearance of the leaves. We make available our CNN code to the research community, as well as the aerial image data set and a hand-made ground truth segmentation with pixel precision to facilitate the comparison among different algorithms.

Download Full-text

The Efficiency of Large Hole Boring (MSP) Method in the Reduction of Blast-Induced Vibration

Applied Sciences ◽

10.3390/app11041814 ◽

2021 ◽

Vol 11 (4) ◽

pp. 1814

Author(s):

Min Seong Kim ◽

Sean Seungwon Lee

Keyword(s):

Urban Areas ◽

Cost Effective ◽

Base Case ◽

Large Hole ◽

Promising Alternative ◽

Geological Information ◽

Drill And Blast ◽

Smooth Blasting ◽

Alternative Designs ◽

Blasting Method

Drill and blast is the most cost-effective excavation method for underground construction, however, vibration and noise, induced by blasting, have been consistently reported as problems. Cut blasting has been widely employed to reduce the blast-induced problems during underground excavation. We propose that the large hole boring method using the state-of-the-art MSP (Multi-setting smart-investigation of the ground and pre-large hole boring) machine (“MSP method”) can efficiently improve vibration reduction. The MSP machine will be used to create 382 mm diameter empty holes at the tunnel cut area for this purpose. This study assessed the efficiency of the MSP method in reducing blast-induced vibration in five blasting patterns using a cylinder-cut, which is a traditional cut blasting method. The controlled blasting patterns using the MSP method demonstrated up to 72% reduction in blast-induced vibration, compared to the base case, Pattern B, where only cylinder-cut and smooth blasting method were applied. Therefore, the MSP method proves to be a promising alternative for blasting in sensitive urban areas where non-vibration excavation techniques were initially considered. Geological characteristics of 50 m beyond the excavation face can be acquired through the proposed real-time boring data monitoring system together with a borehole alignment tracking and ground exploration system. The obtained geological information will be a great help in preparing alternative designs, and scheduling of construction equipment and labour during the tunnel construction.

Download Full-text

A Multi-Task Network with Distance–Mask–Boundary Consistency Constraints for Building Extraction from Aerial Images

Remote Sensing ◽

10.3390/rs13142656 ◽

2021 ◽

Vol 13 (14) ◽

pp. 2656

Author(s):

Furong Shi ◽

Tong Zhang

Keyword(s):

Distance Estimation ◽

Image Data ◽

Learning Technologies ◽

Aerial Images ◽

Superior Performance ◽

Aerial Image ◽

Great Success ◽

Building Extraction ◽

Shape Information ◽

Multi Scale

Deep-learning technologies, especially convolutional neural networks (CNNs), have achieved great success in building extraction from areal images. However, shape details are often lost during the down-sampling process, which results in discontinuous segmentation or inaccurate segmentation boundary. In order to compensate for the loss of shape information, two shape-related auxiliary tasks (i.e., boundary prediction and distance estimation) were jointly learned with building segmentation task in our proposed network. Meanwhile, two consistency constraint losses were designed based on the multi-task network to exploit the duality between the mask prediction and two shape-related information predictions. Specifically, an atrous spatial pyramid pooling (ASPP) module was appended to the top of the encoder of a U-shaped network to obtain multi-scale features. Based on the multi-scale features, one regression loss and two classification losses were used for predicting the distance-transform map, segmentation, and boundary. Two inter-task consistency-loss functions were constructed to ensure the consistency between distance maps and masks, and the consistency between masks and boundary maps. Experimental results on three public aerial image data sets showed that our method achieved superior performance over the recent state-of-the-art models.

Download Full-text

High Diversity in Urban Areas: How Comprehensive Sampling Reveals High Ant Species Richness within One of the Most Urbanized Regions of the World

Diversity ◽

10.3390/d13080358 ◽

2021 ◽

Vol 13 (8) ◽

pp. 358

Author(s):

François Brassard ◽

Chi-Man Leong ◽

Hoi-Hou Chan ◽

Benoit Guénard

Keyword(s):

Exotic Species ◽

Urban Areas ◽

Urban Landscapes ◽

Dual Roles ◽

Ant Communities ◽

Continuous Increase ◽

Tropical Regions ◽

The World ◽

Ant Diversity ◽

The Vertical Distribution

The continuous increase in urbanization has been perceived as a major threat for biodiversity, particularly within tropical regions. Urban areas, however, may still provide opportunities for conservation. In this study focused on Macao (China), one of the most densely populated regions on Earth, we used a comprehensive approach, targeting all the vertical strata inhabited by ants, to document the diversity of both native and exotic species, and to produce an updated checklist. We then compared these results with 112 studies on urban ants to illustrate the dual roles of cities in sustaining ant diversity and supporting the spread of exotic species. Our study provides the first assessment on the vertical distribution of urban ant communities, allowing the detection of 55 new records in Macao, for a total of 155 ant species (11.5% being exotic); one of the highest species counts reported for a city globally. Overall, our results contrast with the dominant paradigm that urban landscapes have limited conservation value but supports the hypothesis that cities act as gateways for exotic species. Ultimately, we argue for a more comprehensive understanding of ants within cities around the world to understand native and exotic patterns of diversity.

Download Full-text

“We Make Our Community”: Youth Forging Environmental Identities in Urban Landscapes

Sustainability ◽

10.3390/su13147736 ◽

2021 ◽

Vol 13 (14) ◽

pp. 7736

Author(s):

Erin Gallay ◽

Alisa Pykett ◽

Constance Flanagan

Keyword(s):

High School Students ◽

Urban Areas ◽

Local Environment ◽

Natural World ◽

Urban Landscapes ◽

Youth Of Color ◽

School Students ◽

Environmental Leadership ◽

And Gender ◽

The Impact

Insofar as race, class, and gender have profound effects on people’s environmental experiences, and consequently their activism, the environmental field needs more work on the environmental experiences and insights of groups whose voices have been missing, including youth of color who live in urban areas in the U.S. In this paper, we focus on African American and Latinx students engaged in environmental projects in their urban communities and the impact of such projects on promoting pro-environmental leadership, agency, and behavior. We draw from written reflections and focus group interviews of several hundred 4th–12th graders (majority middle- and high-school students) who participated in place-based civic science projects. Thematic analyses of student responses found that students engaged in work on local environmental issues cultivated an appreciation for the natural world and an understanding of human-nature interdependence and the ties between the local environment and their communities’ health. Through taking action with others in their communities, students viewed themselves as contributors to their communities and started to form environmental identities in ways that are not traditionally measured. Findings point to the need for forms of environmental education that are contextually grounded and centered on environmental justice in urban areas.

Download Full-text

Predicting Aedes aegypti infestation using landscape and thermal features

Scientific Reports ◽

10.1038/s41598-020-78755-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Camila Lorenz ◽

Marcia C. Castro ◽

Patricia M. P. Trindade ◽

Maurício L. Nogueira ◽

Mariana de Oliveira Lage ◽

...

Keyword(s):

Remote Sensing ◽

Aedes Aegypti ◽

Vector Control ◽

Land Surface ◽

Urban Areas ◽

Negative Binomial ◽

Control Strategies ◽

Winter Season ◽

Landsat 8 ◽

Thermal Features

AbstractIdentifying Aedes aegypti breeding hotspots in urban areas is crucial for the design of effective vector control strategies. Remote sensing techniques offer valuable tools for mapping habitat suitability. In this study, we evaluated the association between urban landscape, thermal features, and mosquito infestations. Entomological surveys were conducted between 2016 and 2019 in Vila Toninho, a neighborhood of São José do Rio Preto, São Paulo, Brazil, in which the numbers of adult female Ae. aegypti were recorded monthly and grouped by season for three years. We used data from 2016 to 2018 to build the model and data from summer of 2019 to validate it. WorldView-3 satellite images were used to extract land cover classes, and land surface temperature data were obtained using the Landsat-8 Thermal Infrared Sensor (TIRS). A multilevel negative binomial model was fitted to the data, which showed that the winter season has the greatest influence on decreases in mosquito abundance. Green areas and pavements were negatively associated, and a higher cover of asbestos roofs and exposed soil was positively associated with the presence of adult females. These features are related to socio-economic factors but also provide favorable breeding conditions for mosquitos. The application of remote sensing technologies has significant potential for optimizing vector control strategies, future mosquito suppression, and outbreak prediction.

Download Full-text