Object-based Automated Mapping of Floodwater in Dense Urban Areas

2020 ◽  
Author(s):  
Ying Zhang
Keyword(s):  
Urban Areas ◽  
Automated Mapping ◽  
Object Based

scholarly journals Mapping Informal Settlements in the Middle East Environment using an Object-Based Machine-Learning Approach

2018 ◽  
Author(s):  
Ahmad Fallatah ◽  
Simon Jones ◽  
David Mitchell
Keyword(s):  
Machine Learning ◽  
Urban Areas ◽  
Hybrid Approach ◽  
Informal Settlements ◽  
Validation Dataset ◽  
Relative Importance ◽  
Object Based Image Analysis ◽  
Object Based ◽  
Machine Learning Approach ◽  
Very High

The identification of informal settlements in urban areas is an important step in developing and implementing pro-poor urban policies. Understanding when, where and who lives inside informal settlements is critical to efforts to improve their resilience. This study aims to analyse the capability of machine-learning (ML) methods to map informal areas in Jeddah, Saudi Arabia, using very-high-resolution (VHR) imagery and terrain data. Fourteen indicators of settlement characteristics were derived and mapped using an object-based ML approach and VHR imagery. These indicators were categorised according to three different spatial levels: environ, settlement and object. The most useful indicators for prediction were found to be density and texture measures, (with random forest (RF) relative importance measures of over 25% and 23% respectively). The success of this approach was evaluated using a small, fully independent validation dataset. Informal areas were mapped with an overall accuracy of 91%. Object-based ML as a hybrid approach performed better (8%) than object-based image analysis alone due to its ability to encompass all available geospatial levels.

scholarly journals A Novel Class-Specific Object-Based Method for Urban Change Detection Using High-Resolution Remote Sensing Imagery

2021 ◽  
Vol 87 (4) ◽  
pp. 249-262
Author(s):  
Ting Bai ◽  
Kaimin Sun ◽  
Wenzhuo Li ◽  
Deren Li ◽  
Yepei Chen ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
High Resolution ◽  
Land Cover ◽  
Change Detection ◽  
Urban Areas ◽  
Support Vector ◽  
Change Type ◽  
Optimal Classifier ◽  
Object Based ◽  
Specific Object

A single-scale object-based change-detection classifier can distinguish only global changes in land cover, not the more granular and local changes in urban areas. To overcome this issue, a novel class-specific object-based change-detection method is proposed. This method includes three steps: class-specific scale selection, class-specific classifier selection, and land cover change detection. The first step combines multi-resolution segmentation and a random forest to select the optimal scale for each change type in land cover. The second step links multi-scale hierarchical sampling with a classifier such as random forest, support vector machine, gradient-boosting decision tree, or Adaboost; the algorithm automatically selects the optimal classifier for each change type in land cover. The final step employs the optimal classifier to detect binary changes and from-to changes for each change type in land cover. To validate the proposed method, we applied it to two high-resolution data sets in urban areas and compared the change-detection results of our proposed method with that of principal component analysis k-means, object-based change vector analysis, and support vector machine. The experimental results show that our proposed method is more accurate than the other methods. The proposed method can address the high levels of complexity found in urban areas, although it requires historical land cover maps as auxiliary data.

scholarly journals Urban Landscape Change Analysis Using Local Climate Zones and Object-Based Classification in the Salt Lake Metro Region, Utah, USA

2019 ◽  
Vol 11 (13) ◽  
pp. 1615 ◽  
Author(s):  
Jed Collins ◽  
Iryna Dronova
Keyword(s):  
Urban Areas ◽  
Salt Lake ◽  
Remote Sensing Data ◽  
Climate Trends ◽  
Local Cooling ◽  
Change Analysis ◽  
Study Region ◽  
Local Climate ◽  
Object Based ◽  
Local Climate Zones

Urban areas globally are vulnerable to warming climate trends exacerbated by their growing populations and heat island effects. The Local Climate Zone (LCZ) typology has become a popular framework for characterizing urban microclimates in different regions using various classification methods, including a widely adopted pixel-based protocol by the World Urban Database and Access Portal Tools (WUDAPT) Project. However, few studies to date have explored the potential of object-based image analysis (OBIA) to facilitate classification of LCZs given their inherent complexity, and few studies have further used the LCZ framework to analyze land cover changes in urban areas over time. This study classified LCZs in the Salt Lake Metro Region, Utah, USA for 1993 and 2017 using a supervised object-based analysis of Landsat satellite imagery and assessed their change during this time frame. The overall accuracy, measured for the most recent classification period (2017), was equal to 64% across 12 LCZs, with most of the error resulting from similarities among highly developed LCZs and non-developed classes with sparse or low-stature vegetation. The observed 1993–2017 changes in LCZs indicated a regional tendency towards primarily suburban, open low-rise development, and large low-rise and paved classes. However, despite the potential for local cooling with landscape transitions likely to increase vegetation cover and irrigation compared to pre-development conditions, summer averages of Landsat-derived top-of-atmosphere brightness temperatures showed a pronounced warming between 1992–1994 and 2016–2018 across the study region, with a 0.1–2.9 °C increase among individual LCZs. Our results indicate that future applications of LCZs towards urban change analyses should develop a stronger understanding of LCZ microclimate sensitivity to changes in size and configuration of urban neighborhoods and regions. Furthermore, while OBIA is promising for capturing the heterogeneous and multi-scale nature of LCZs, its applications could be strengthened by adopting more generalizable approaches for LCZ-relevant segmentation and validation, and by incorporating active remote sensing data to account for the 3D complexity of urban areas.

scholarly journals Land Cover/Land Use Mapping of LISS IV Imagery Using Object-Based Convolutional Neural Network with Deep Features

2019 ◽  
Vol 48 (1) ◽  
pp. 145-154
Author(s):  
S. Rajesh ◽  
T. Gladima Nisia ◽  
S. Arivazhagan ◽  
R. Abisekaraj
Keyword(s):  
Deep Learning ◽  
Classification Accuracy ◽  
Urban Areas ◽  
Satellite Images ◽  
Feature Learning ◽  
Remotely Sensed ◽  
Learning Method ◽  
Deep Feature ◽  
Object Based ◽  
Remotely Sensed Images

Abstract The paper proposes a new method for classifying the LISS IV satellite images using deep learning method. Deep learning method is to automatically extract many features without any human intervention. The classification accuracy through deep learning is still improved by including object-based segmentation. The object-based deep feature learning method using CNN is used to accurately classify the remotely sensed images. The method is designed with the technique of extracting the deep features and using it for object-based classification. The proposed system extracts deep features using pre-defined filter values, thus increasing the overall performance of the process compared to randomly initialized filter values. The object-based classification method can preserve edge information in complex satellite images. To improve the classification accuracy and to reduce complexity, object-based deep learning technique is used. The proposed object-based deep learning approach is used to drastically increase the classification accuracy. Here, the remotely sensed images were used to classify the urban areas of Ahmadabad and Madurai cities. Experimental results show a better performance with the object-based classification.

Determination of Urban Growth by the Night-Time Images

2018 ◽  
pp. 7831-7842
Author(s):  
Emre Yücer ◽  
Arzu Erener
Keyword(s):  
Image Analysis ◽  
Urban Growth ◽  
Urban Areas ◽  
Dynamic Structure ◽  
Current Data ◽  
Urban Region ◽  
Night Time ◽  
Object Based ◽  
Us Air Force

Urbanization in Turkey and in the world continues is increased especially in the last 30 years. Therefore, urban areas having a dynamic structure should be regularly monitored and the urban region should be determined. There is a need of accurate current data that enables to monitor the temporally changing urban development and land use at regular intervals. In order to determine the urban sizes the 2010 DMSP - OLS (US Air Force Meteorological Satellite Program – operational line scan system) night-time images are used. This study is adapted to all provinces of Turkey. Night-time images in the literature are mainly used for examining the global economic and demographic differences between countries. In order to determine the urban growth from night-time images two different image analysis including object-based and cell-based methods are used in this study. Object-oriented image analysis method includes image segmentation studies and cell-based classification method contains natural breaks and otsu method.

scholarly journals Building Instance Change Detection from Large-Scale Aerial Images using Convolutional Neural Networks and Simulated Samples

2019 ◽  
Vol 11 (11) ◽  
pp. 1343 ◽  
Author(s):  
Shunping Ji ◽  
Yanyun Shen ◽  
Meng Lu ◽  
Yongjun Zhang
Keyword(s):  
Deep Learning ◽  
Change Detection ◽  
Urban Areas ◽  
Large Scale ◽  
Real Life ◽  
Aerial Images ◽  
Building Extraction ◽  
Object Based ◽  
Wide Range ◽  
Change Map

We present a novel convolutional neural network (CNN)-based change detection framework for locating changed building instances as well as changed building pixels from very high resolution (VHR) aerial images. The distinctive advantage of the framework is the self-training ability, which is highly important in deep-learning-based change detection in practice, as high-quality samples of changes are always lacking for training a successful deep learning model. The framework consists two parts: a building extraction network to produce a binary building map and a building change detection network to produce a building change map. The building extraction network is implemented with two widely used structures: a Mask R-CNN for object-based instance segmentation, and a multi-scale full convolutional network for pixel-based semantic segmentation. The building change detection network takes bi-temporal building maps produced from the building extraction network as input and outputs a building change map at the object and pixel levels. By simulating arbitrary building changes and various building parallaxes in the binary building map, the building change detection network is well trained without real-life samples. This greatly lowers the requirements of labeled changed buildings, and guarantees the algorithm’s robustness to registration errors caused by parallaxes. To evaluate the proposed method, we chose a wide range of urban areas from an open-source dataset as training and testing areas, and both pixel-based and object-based model evaluation measures were used. Experiments demonstrated our approach was vastly superior: without using any real change samples, it reached 63% average precision (AP) at the object (building instance) level. In contrast, with adequate training samples, other methods—including the most recent CNN-based and generative adversarial network (GAN)-based ones—have only reached 25% AP in their best cases.

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