A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change

Reba, Meredith; Seto, Karen C.

doi:10.1016/j.rse.2020.111739

Cited by 139 publications

(69 citation statements)

References 124 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the consequent iterations, additional actively selected 0.1% samples were put in to form the new set for training. The size of scene patch was set as 10 × 10 pixels, while the moving window and the radius of disk-shaped structuring element for GLCM and MPs were 5 × 5 pixels and [4,5,6]. For controlling variables, the size of the input patches in AlexCD and SiamCD was also set as 5 × 5.…”

Section: ) Sentinel-2 Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Change Detection Based on Low-Level to High-Level Features Integration With Limited Samples

Wang

Chen

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

Detailed land cover change in multi-temporal images is an important application for earth science. Many techniques have been proposed to solve this problem in different ways. However, accurately identifying changes still remains a challenge due to the difficulties in describing the characteristics of various change categories by using single level features. In this paper, a multi-level feature representation framework was designed to build robust feature set for complex change detection task. First, four different levels of information from low level to high level, including pixel-level, neighborhood-level, object-level, and scenelevel features, were extracted. Through the operation of extracting different level features from multi-temporal images, the differences between them can be described comprehensively. Second, multi-level features were fused to reduce the dimension and then used as the input for supervised change detector with initial limited labels. Finally, for reducing the labeling cost and improving the change detection results simultaneously, active learning was conducted to select the most informative samples for labeling, and this step together with the previous steps were iteratively conducted to improve the results in each round. Experimental results of three pairs of real remote sensing datasets demonstrated that the proposed framework outperformed the other state-of-the-art methods in terms of accuracy. Moreover, the influences of scene scale for high-level semantic features in the proposed approach on change detection performance were also analyzed and discussed.

show abstract

Section: ) Sentinel-2 Datasetmentioning

confidence: 99%

“…Various change detection techniques through multi-temporal remote sensing images have been proposed [2][3][4][5]. They are widely used in different applications, including natural (e.g., wildfires and glacial retreat) and anthropogenic disturbances (e.g., deforestation and urbanization) [6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Change Detection Based on Low-Level to High-Level Features Integration With Limited Samples

Wang

Chen

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Another limitation to this study was that of the spatial resolution of the images used in this study. Perhaps, higher spatial resolution images could have impacted on the producers, users and overall accuracy assessment [14,15].…”

Section: Post-classification Change Detectionmentioning

confidence: 99%

Assessing Urban Land use Change in New Braunfels, Texas from 2013 to 2020

Lasode¹,

Esobi²,

Anyanwu³

et al. 2020

Preprint

View full text Add to dashboard Cite

Urban cities are the major drivers of economic growth and development. Economic growth and development however results in considerable land cover land use dynamics. This study assessed the dynamics in land cover land use that have occurred in New Braunfels, Texas in the last 7 years (2013 - 2020) to observe areas in the city that had experienced considerable shifts in land cover and land use. A 30-meter resolution Landsat images were used to examine possible changes in land cover land use. New Braunfels was observed to have experienced significant changes in land use especially in developed areas. This change can be attributed to the influx of people into the city, contributing to the need for increased urban development. Analysis of this study shows that about 16% (about 553 hectares) of forest land cover class and 28% (about 1,139 hectares) of grassland class in time 1 (August 31, 2013) changed to built-up land use class in time 2 (November 5, 2020). A limitation to this study was that of the spatial resolution of images used. Higher spatial resolution images could impact the producers, users, and overall accuracy assessment. Results from this study can aid in supporting better decision-making for sustainable urban development and climate change mitigation.

show abstract

“…The timely and accurate urban land use information is important for guiding urban planning and land use management [2]. Remote sensing (RS) techniques were widely used to update urban land use information over the past few decades, by referring to the differences in aspects of texture, spectrum, and context among urban land use categories [3,4]. However, due to the high similarity among urban land use categories in physical attributes, it is hard to identify the complexity and diversity of urban internal structures [5,6], especially in cities with highdensity populations and buildings, such as Hangzhou, Beijing, Shanghai, and Shenzhen [7].…”

Section: Introductionmentioning

confidence: 99%

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Yin

Hamm

et al. 2021

Remote Sensing

View full text Add to dashboard Cite

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.

show abstract

A systematic review and assessment of algorithms to detect, characterize, and monitor urban land change

Cited by 139 publications

References 124 publications

Change Detection Based on Low-Level to High-Level Features Integration With Limited Samples

Change Detection Based on Low-Level to High-Level Features Integration With Limited Samples

Assessing Urban Land use Change in New Braunfels, Texas from 2013 to 2020

Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Contact Info

Product

Resources

About