Large urban zone classification on SPOT-5 imagery with convolutional neural networks

Krylov, Vladimir A.; Martino, Michaela De; Moser, Gabriele

doi:10.1109/igarss.2016.7729461

Cited by 9 publications

(9 citation statements)

References 6 publications

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“…In order to create custom datasets, labeling by hand is the most common and accurate but also labor-intensive way, whereas creating datasets from synthetic data is fast, generic and offers an inexpensive alternative as shown by Isikdogan et al [48] and Kong et al [49]. Since synthetic data is hard to create for multispectral and radar remote sensing, weakly supervised approaches [50][51][52] and studies that leverage OSM (Open Street Map) data [53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68] offer insights in how to use fuzzy data sources [69]. Thus, researchers are encouraged to use such approaches or small-scale hand-labeled datasets for proof-of-concept studies in order to build custom, large-scale, deep-learning datasets in the next step.…”

Section: Datasets Usedmentioning

confidence: 99%

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020

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In Earth observation (EO), large-scale land-surface dynamics are traditionally analyzed by investigating aggregated classes. The increase in data with a very high spatial resolution enables investigations on a fine-grained feature level which can help us to better understand the dynamics of land surfaces by taking object dynamics into account. To extract fine-grained features and objects, the most popular deep-learning model for image analysis is commonly used: the convolutional neural network (CNN). In this review, we provide a comprehensive overview of the impact of deep learning on EO applications by reviewing 429 studies on image segmentation and object detection with CNNs. We extensively examine the spatial distribution of study sites, employed sensors, used datasets and CNN architectures, and give a thorough overview of applications in EO which used CNNs. Our main finding is that CNNs are in an advanced transition phase from computer vision to EO. Upon this, we argue that in the near future, investigations which analyze object dynamics with CNNs will have a significant impact on EO research. With a focus on EO applications in this Part II, we complete the methodological review provided in Part I.

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Section: Datasets Usedmentioning

confidence: 99%

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

2020

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“…In recent years, deep neural network has shown excellent performance in the task of remote sensing image classification and segmentation [16,[39][40][41]. Feature maps output from different layers in neural network reflect different characteristics of remote sensing images.…”

Section: Urban Functional Regions Classification Based On Remote Sensmentioning

confidence: 99%

A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

Qing

Han³

et al. 2020

Remote Sensing

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For urban planning and environmental monitoring, it is essential to understand the diversity and complexity of cities to identify urban functional regions accurately and widely. However, the existing methods developed in the literature for identifying urban functional regions have mainly been focused on single remote sensing image data or social sensing data. The multi-dimensional information which was attained from various data source and could reflect the attribute or function about the urban functional regions that could be lost in some extent. To sense urban functional regions comprehensively and accurately, we developed a multi-mode framework through the integration of spatial geographic characteristics of remote sensing images and the functional distribution characteristics of social sensing data of Point-of-Interest (POI). In this proposed framework, a deep multi-scale neural network was developed first for the functional recognition of remote sensing images in urban areas, which explored the geographic feature information implicated in remote sensing. Second, the POI function distribution was analyzed in different functional areas of the city, then the potential relationship between POI data categories and urban region functions was explored based on the distance metric. A new RPF module is further deployed to fuse the two characteristics in different dimensions and improve the identification performance of urban region functions. The experimental results demonstrated that the proposed method can efficiently achieve the accuracy of 82.14% in the recognition of functional regions. It showed the great usability of the proposed framework in the identification of urban functional regions and the potential to be applied in a wide range of areas.

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“…Iino et al proposed a deep learning approach to study the urban changes in developing countries [180]. Other studies were conducted to compare the accuracy from CNN and Random Forest methods in urban change classification [172,181]. In most comparison studies, deep learning always results in higher classification accuracy [87,180].…”

Section: Modeling Gentrification Using Deep Learning and Time-series Remote Sensingmentioning

confidence: 99%

“…Many scientists have noticed that the interest in using deep learning for remote sensing applications is rising rapidly [172,178,181,191]. However, the real-world applications of using deep learning in analyzing time-series remote sensing patterns are still scarce [182].…”

Section: Limitations Of Current Gentrification Mappingmentioning

confidence: 99%

“…Field surveys and investigations [85]; evaluation of culture and history [24,157] Both methodologies and results are easy to understand [24,157,158] Require knowledgeful investigators [58,85]; high financial cost; labor-intensive and low scalability [8,151] Conventional Statistical correlation models [11,58,88,91,96,97,[153][154][155]160]; transfer learning [134][135][136][137][138][140][141][142][143]161,162] Models are easy to use and replicable [97,167,168]; low financial cost [85] Too many algorithms [96][97][98]; results are not reliable [96,153] Novel Machine learning imagery classification [95,99,[120][121][122]181,…”

Section: Obsoletementioning

confidence: 99%

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Remote Sensing of Urban Poverty and Gentrification

Lin

Zhang

et al. 2021

Remote Sensing

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In the past few decades, most urban areas in the world have been facing the pressure of an increasing population living in poverty. A recent study has shown that up to 80% of the population of some cities in Africa fall under the poverty line. Other studies have shown that poverty is one of the main contributors to residents’ poor health and social conflict. Reducing the number of people living in poverty and improving their living conditions have become some of the main tasks for many nations and international organizations. On the other hand, urban gentrification has been taking place in the poor neighborhoods of all major cities in the world. Although gentrification can reduce the poverty rate and increase the GDP and tax revenue of cities and potentially bring opportunities for poor communities, it displaces the original residents of the neighborhoods, negatively impacting their living and access to social services. In order to support the sustainable development of cities and communities and improve residents’ welfare, it is essential to identify the location, scale, and dynamics of urban poverty and gentrification, and remote sensing can play a key role in this. This paper reviews, summarizes, and evaluates state-of-the-art approaches for identifying and mapping urban poverty and gentrification with remote sensing, GIS, and machine learning techniques. It also discusses the pros and cons of remote sensing approaches in comparison with traditional approaches. With remote sensing approaches, both spatial and temporal resolutions for the identification of poverty and gentrification have been dramatically increased, while the economic cost is significantly reduced.

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Large urban zone classification on SPOT-5 imagery with convolutional neural networks

Cited by 9 publications

References 6 publications

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

Object Detection and Image Segmentation with Deep Learning on Earth Observation Data: A Review—Part II: Applications

A New Remote Sensing Images and Point-of-Interest Fused (RPF) Model for Sensing Urban Functional Regions

Remote Sensing of Urban Poverty and Gentrification

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