Vehicle detection in urban areas from very high resolution UAV color images

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.

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Remote sensing model adaptation to very high resolution digital images of urban areas

IEEE/ISPRS Joint Workshop on Remote Sensing and Data Fusion over Urban Areas (Cat. No.01EX482) ◽

10.1109/dfua.2001.985718 ◽

2002 ◽

Cited By ~ 3

Author(s):

D. Boldo ◽

H. Le Men

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Urban Areas ◽

Digital Images ◽

Model Adaptation ◽

Remote Sensing Model ◽

Sensing Model ◽

Very High

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Erratum to ‘Hierarchical object oriented classification using very high resolution imagery and LIDAR data over urban areas’

Advances in Space Research ◽

10.1016/j.asr.2012.01.003 ◽

2012 ◽

Vol 49 (6) ◽

pp. 1112 ◽

Cited By ~ 2

Author(s):

Yunhao Chen ◽

Wei Su ◽

Jing Li ◽

Zhongping Sun

Keyword(s):

High Resolution ◽

Urban Areas ◽

Object Oriented ◽

Lidar Data ◽

High Resolution Imagery ◽

Very High Resolution Imagery ◽

Very High

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

10.5194/ems2021-53 ◽

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

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.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 &#201;ireann 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 &#201;ireann to evaluate solutions to improve the land-cover map in HARMONIE-AROME.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.&#160;[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[2] Bengtsson, L., Andrae, U., Aspelien, T., Batrak, Y., Calvo, J., de Rooy, W.,. . . K&#248;ltzow, M. &#216;. (2017). The HARMONIE&#8211;AROME Model Configurationin the ALADIN&#8211;HIRLAM NWP System. Monthly Weather Review, 145(5),1919&#8211;1935.https://doi.org/10.1175/mwr-d-16-0417.1[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&#8217;s Optical High-ResolutionMission for GMES Operational Services, ESA bulletin. Bulletin ASE. Euro-pean Space Agency, SP-1322,2012[4] Ulmas, P. and Liiv, I. (2020). Segmentation of Satellite Imagery using U-Net Models for Land Cover Classification, pp. 1&#8211;11,http://arxiv.org/abs/2003.02899, 2020[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)

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Towards Sub-Pixel Automatic Geometric Corrections of Very-High Resolution Panchromatic Satellite Data of Urban Areas

Remote Sensing ◽

10.3390/rs11091097 ◽

2019 ◽

Vol 11 (9) ◽

pp. 1097 ◽

Cited By ~ 1

Author(s):

Aleš Marsetič ◽

Peter Pehani

Keyword(s):

Feature Extraction ◽

High Resolution ◽

Urban Areas ◽

Satellite Images ◽

Reference Data ◽

Geometric Model ◽

Control Point ◽

Satellite Image ◽

Extraction Algorithm ◽

Very High

This paper presents an automatic procedure for the geometric corrections of very-high resolution (VHR) optical panchromatic satellite images. The procedure is composed of three steps: an automatic ground control point (GCP) extraction algorithm that matches the linear features that were extracted from the satellite image and reference data; a geometric model that applies a rational function model; and, the orthorectification procedure. Accurate geometric corrections can only be achieved if GCPs are employed to precisely correct the geometric biases of images. Due to the high resolution and the varied acquisition geometry of images, we propose a fast, segmentation based method for feature extraction. The research focuses on densely populated urban areas, which are very challenging in terms of feature extraction and matching. The proposed algorithm is capable of achieving results with a root mean square error of approximately one pixel or better, on a test set of 14 panchromatic Pléiades images. The procedure is robust and it performs well in urban areas, even for images with high off-nadir angles.

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