3D modeling of large urban areas with stereo VHR satellite imagery: lessons learned

2013 ◽  
Vol 68 (1) ◽  
pp. 53-78 ◽  
Author(s):  
Daniela Poli ◽  
Ivano Caravaggi
Keyword(s):  
Satellite Imagery ◽  
3D Modeling ◽  
Urban Areas ◽  
Lessons Learned

scholarly journals The “Paris-End” of Town? Deriving Urban Typologies Using Three Imagery Types

Urban Science ◽  
2020 ◽  
Vol 4 (2) ◽  
pp. 27
Author(s):  
Kerry A. Nice ◽  
Jason Thompson ◽  
Jasper S. Wijnands ◽  
Gideon D. P. A. Aschwanden ◽  
Mark Stevenson
Keyword(s):  
Neural Networks ◽  
Satellite Imagery ◽  
Urban Form ◽  
Urban Areas ◽  
Global Scale ◽  
Lessons Learned ◽  
Targeted Interventions ◽  
Advantages And Disadvantages ◽  
Street View ◽  
Social Demographic

Urban typologies allow areas to be categorised according to form and the social, demographic, and political uses of the areas. The use of these typologies and finding similarities and dissimilarities between cities enables better targeted interventions for improved health, transport, and environmental outcomes in urban areas. A better understanding of local contexts can also assist in applying lessons learned from other cities. Constructing urban typologies at a global scale through traditional methods, such as functional or network analysis, requires the collection of data across multiple political districts, which can be inconsistent and then require a level of subjective classification. To overcome these limitations, we use neural networks to analyse millions of images of urban form (consisting of street view, satellite imagery, and street maps) to find shared characteristics between the largest 1692 cities in the world. The comparison city of Paris is used as an exemplar and we perform a case study using two Australian cities, Melbourne and Sydney, to determine if a “Paris-end” of town exists or can be found in these cities using these three big data imagery sets. The results show specific advantages and disadvantages of each type of imagery in constructing urban typologies. Neural networks trained with map imagery will be highly influenced by the structural mix of roads, public transport, and green and blue space. Satellite imagery captures a combination of both urban form and decorative and natural details. The use of street view imagery emphasises the features of a human-scaled visual geography of streetscapes. However, for both satellite and street view imagery to be highly effective, a reduction in scale and more aggressive pre-processing might be required in order to reduce detail and create greater abstraction in the imagery.

scholarly journals Spatial-Temporal Land Use and Land Cover Changes in Urban Areas Using Remote Sensing Images and GIS Analysis: The Case Study of Opole, Poland

Geosciences ◽  
2021 ◽  
Vol 11 (8) ◽  
pp. 312
Author(s):  
Barbara Wiatkowska ◽  
Janusz Słodczyk ◽  
Aleksandra Stokowska
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Agricultural Land ◽  
Urban Expansion ◽  
Landsat 8 ◽  
Rate Of Increase ◽  
Management Procedures

Urban expansion is a dynamic and complex phenomenon, often involving adverse changes in land use and land cover (LULC). This paper uses satellite imagery from Landsat-5 TM, Landsat-8 OLI, Sentinel-2 MSI, and GIS technology to analyse LULC changes in 2000, 2005, 2010, 2015, and 2020. The research was carried out in Opole, the capital of the Opole Agglomeration (south-western Poland). Maps produced from supervised spectral classification of remote sensing data revealed that in 20 years, built-up areas have increased about 40%, mainly at the expense of agricultural land. Detection of changes in the spatial pattern of LULC showed that the highest average rate of increase in built-up areas occurred in the zone 3–6 km (11.7%) and above 6 km (10.4%) from the centre of Opole. The analysis of the increase of built-up land in relation to the decreasing population (SDG 11.3.1) has confirmed the ongoing process of demographic suburbanisation. The paper shows that satellite imagery and GIS can be a valuable tool for local authorities and planners to monitor the scale of urbanisation processes for the purpose of adapting space management procedures to the changing environment.

Identification of Potential Freight Parking Locations in Urban Areas

2021 ◽  
pp. 036119812110130
Author(s):  
Glenn Vorhes ◽  
Ernest Perry ◽  
Soyoung Ahn
Keyword(s):  
United States ◽  
Information System ◽  
Satellite Imagery ◽  
Urban Areas ◽  
The United States ◽  
Truck Drivers ◽  
Short List ◽  
Automated Processes ◽  
Parking Facilities ◽  
Urban Sites

Truck parking is a crucial element of the United States’ transportation system as it provides truckers with safe places to rest and stage for deliveries. Demand for truck parking spaces exceeds supply and shortages are especially common in and around urban areas. Freight operations are negatively affected as truck drivers are unable to park in logistically ideal locations. Drivers may resort to unsafe practices such as parking on ramps or in abandoned lots. This report seeks to examine the potential parking availability of vacant urban parcels by establishing a methodology to identify parcels and examining whether the identified parcels are suitable for truck parking. Previous research has demonstrated that affordable, accessible parcels are available to accommodate truck parking. When used in conjunction with other policies, adaptation of urban sites could help reduce the severity of truck parking shortages. Geographic information system parcel and roadway data were obtained for one urban area in each of the 10 Mid America Association of Transportation Officials region states. Area and proximity filters were applied followed by spectral analysis of satellite imagery to identify candidate parcels for truck parking facilities within urban areas. The automated processes created a ranked short list of potential parcels from which those best suited for truck parking could be efficiently identified for inspection by satellite imagery. This process resulted in a manageable number of parcels to be evaluated further by local knowledge metrics such as availability and cost, existing infrastructure and municipal connections, and safety.

scholarly journals Meeting the Challenges of Longitudinal Cluster-Based Trials in Schools: Lessons From the Chicago Trial of Positive Action

2016 ◽  
Vol 40 (4) ◽  
pp. 450-482 ◽  
Author(s):  
Kendra M. Lewis ◽  
David L. DuBois ◽  
Peter Ji ◽  
Joseph Day ◽  
Naida Silverthorn ◽  
...  
Keyword(s):  
Public Schools ◽  
Low Income ◽  
Urban Areas ◽  
Statistical Power ◽  
Student Mobility ◽  
Controlled Trial ◽  
Lessons Learned ◽  
Study Planning ◽  
Positive Action ◽  
Cluster Randomized

We describe challenges in the 6-year longitudinal cluster randomized controlled trial (CRCT) of Positive Action (PA), a social–emotional and character development (SECD) program, conducted in 14 low-income, urban Chicago Public Schools. Challenges pertained to logistics of study planning (school recruitment, retention of schools during the trial, consent rates, assessment of student outcomes, and confidentiality), study design (randomization of a small number of schools), fidelity (implementation of PA and control condition activities), and evaluation (restricted range of outcomes, measurement invariance, statistical power, student mobility, and moderators of program effects). Strategies used to address the challenges within each of these areas are discussed. Incorporation of lessons learned from this study may help to improve future evaluations of longitudinal CRCTs, especially those that involve evaluation of school-based interventions for minority populations and urban areas.

scholarly journals Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery in Google Earth and Microsoft Bing Maps as a Source of Reference Data

Land ◽  
2018 ◽  
Vol 7 (4) ◽  
pp. 118 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Reference Data ◽  
Temporal Distribution ◽  
Google Earth ◽  
Training Data ◽  
Visual Interpretation ◽  
The Usa ◽  
Very High

Very high resolution (VHR) satellite imagery from Google Earth and Microsoft Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, one use of this imagery is to create reference data sets through visual interpretation, e.g., to complement existing training data or to aid in the validation of land-cover products. Through new applications such as Collect Earth, this imagery is also being used for monitoring purposes in the form of statistical surveys obtained through visual interpretation. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global overview of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Microsoft Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India, and with clear discontinuities at political borders. We also show that the availability of VHR imagery is currently not adequate for monitoring protected areas and deforestation, but is better suited for monitoring changes in cropland or urban areas using visual interpretation.

scholarly journals Using machine learning to produce a very high-resolution land-cover map for Ireland and beyond

2021 ◽  
Author(s):  
Geoffrey Bessardon ◽  
Emily Gleeson ◽  
Eoin Walsh
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Weather Forecasting ◽  
Land Cover Classification ◽  
Surface Processes ◽  
Land Cover Map ◽  
Very High

<p>An accurate representation of surface processes is essential for weather forecasting as it is where most of the thermal, turbulent and humidity exchanges occur. The Numerical Weather Prediction (NWP) system, to represent these exchanges, requires a land-cover classification map to calculate the surface parameters used in the turbulent, radiative, heat, and moisture fluxes estimations.</p><p>The land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAM NWP system for operational weather forecasting is ECOCLIMAP. ECOCLIMAP-SG (ECO-SG), the latest version of ECOCLIMAP, was evaluated over Ireland to prepare ECO-SG implementation in HARMONIE-AROME. This evaluation suggested that sparse urban areas are underestimated and instead appear as vegetation areas in ECO-SG [1], with an over-classification of grassland in place of sparse urban areas and other vegetation covers (Met Éireann internal communication). Some limitations in the performance of the current HARMONIE-AROME configuration attributed to surface processes and physiography issues are well-known [2]. This motivated work at Met Éireann to evaluate solutions to improve the land-cover map in HARMONIE-AROME.</p><p>In terms of accuracy, resolution, and the future production of time-varying land-cover map, the use of a convolutional neural network (CNN) to create a land-cover map using Sentinel-2 satellite imagery [3] over Estonia [4] presented better potential outcomes than the use of local datasets [5]. Consequently, this method was tested over Ireland and proven to be more accurate than ECO-SG for representing CORINE Primary and Secondary labels and at a higher resolution [5]. This work is a continuity of [5] focusing on 1. increasing the number of labels, 2. optimising the training procedure, 3. expanding the method for application to other HIRLAM countries and 4. implementation of the new land-cover map in HARMONIE-AROME.</p><p> </p><p>[1] Bessardon, G., Gleeson, E., (2019) Using the best available physiography to improve weather forecasts for Ireland. In EMS Annual Meeting.Retrieved fromhttps://presentations.copernicus.org/EMS2019-702_presentation.pdf</p><p>[2] Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,. . . Køltzow, M. Ø. (2017). The HARMONIE–AROME Model Configurationin the ALADIN–HIRLAM NWP System. Monthly Weather Review, 145(5),1919–1935.https://doi.org/10.1175/mwr-d-16-0417.1</p><p>[3] Bertini, F., Brand, O., Carlier, S., Del Bello, U., Drusch, M., Duca, R., Fernandez, V., Ferrario, C., Ferreira, M., Isola, C., Kirschner, V.,Laberinti, P., Lambert, M., Mandorlo, G., Marcos, P., Martimort, P., Moon, S., Oldeman,P., Palomba, M., and Pineiro, J.: Sentinel-2ESA’s Optical High-ResolutionMission for GMES Operational Services, ESA bulletin. Bulletin ASE. Euro-pean Space Agency, SP-1322,2012</p><p>[4] Ulmas, P. and Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification, pp. 1–11,http://arxiv.org/abs/2003.02899, 2020</p><p>[5] Walsh, E., Bessardon, G., Gleeson, E., and Ulmas, P. (2021). Using machine learning to produce a very high-resolution land-cover map for Ireland. Advances in Science and Research, (accepted for publication)</p>

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