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 Characterizing the Spatial and Temporal Availability of Very High Resolution Satellite Imagery for Monitoring Applications

2018 ◽  
Author(s):  
Myroslava Lesiv ◽  
Linda See ◽  
Juan Carlos Laso Bayas ◽  
Tobias Sturn ◽  
Dmitry Schepaschenko ◽  
...  
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Urban Areas ◽  
Temporal Distribution ◽  
Google Earth ◽  
Arts And Humanities ◽  
The Usa ◽  
High Resolution Satellite Imagery ◽  
Land Cover Maps ◽  
Very High

Abstract. Very high resolution (VHR) satellite imagery from Google Earth and Bing Maps is increasingly being used in a variety of applications from computer sciences to arts and humanities. In the field of remote sensing, this imagery is used to create detailed time-sensitive maps, e.g. for emergency response purposes, or to validate coarser resolution products such as global land cover maps. However, little is known about where VHR satellite imagery exists globally or the dates of the imagery. Here we present a global snapshot of the spatial and temporal distribution of VHR satellite imagery in Google Earth and Bing Maps. The results show an uneven availability globally, with biases in certain areas such as the USA, Europe and India. 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. Supplementary data are available at https://doi.org/10.1594/PANGAEA.885767.

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>

Collecting training data to map forest management at global scale

2021 ◽  
Author(s):  
Myroslava Lesiv ◽  
Dmitry Schepaschenko ◽  
Martina Dürauer ◽  
Marcel Buchhorn ◽  
Ivelina Georgieva ◽  
...  
Keyword(s):  
Forest Management ◽  
High Resolution ◽  
Remotely Sensed ◽  
Global Scale ◽  
Google Earth ◽  
Training Data ◽  
Tree Cover ◽  
Data Set ◽  
The World ◽  
Very High

<p>Spatially explicit information on forest management at a global scale is critical for understanding the current status of forests for sustainable forest management and restoration. Whereas remotely sensed based datasets, developed by applying ML and AI algorithms, can successfully depict tree cover and other land cover types, it has not yet been used to depict untouched forest and different degrees of forest management. We show for the first time that with sufficient training data derived from very high-resolution imagery a differentiation within the tree cover class of various levels of forest management is possible.</p><p>In this session, we would like to present our approach for labeling forest related training data by using Geo-Wiki application (https://www.geo-wiki.org/). Moreover, we would like to share a new open global training data set on forest management we collected from a series of Geo-Wiki campaigns. In February 2019, we organized an expert workshop to (1) discuss the variety of forest management practices that take place in different parts of the world; (2) generalize the definitions for the application at global scale; (3) finalize the Geo-Wiki interface for the crowdsourcing campaigns; and (4) build a data set of control points (or the expert data set), which we used later to monitor the quality of the crowdsourced contributions by the volunteers. We involved forest experts from different regions around the world to explore what types of forest management information could be collected from visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, in combination with Sentinel time series and Normalized Difference Vegetation Index (NDVI) profiles derived from Google Earth Engine (GEE). Based on the results of this analysis, we expanded these campaigns by involving a broader group of participants, mainly people recruited from remote sensing, geography and forest research institutes and universities.</p><p>In total, we collected forest data for approximately 230 000 locations globally. These data are of sufficient density and quality and therefore could be used in many ML and AI applications for forests at regional and local scale.  We also provide an example of ML application, a remotely sensed based global forest management map at a 100 m resolution (PROBA-V) for the year 2015. It includes such classes as intact forests, forests with signs of human impact, including clear cuts and logging, replanted forest, woody plantations with a rotation period up to 15 years, oil palms and agroforestry. The results of independent statistical validation show that the map’s overall accuracy is 81%.</p>

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

2019 ◽  
Vol 11 (9) ◽  
pp. 1097 ◽  
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.

scholarly journals DEEP LEARNING BASED ROOF TYPE CLASSIFICATION USING VERY HIGH RESOLUTION AERIAL IMAGERY

2021 ◽  
Vol XLIII-B3-2021 ◽  
pp. 55-60
Author(s):  
M. Buyukdemircioglu ◽  
R. Can ◽  
S. Kocaman
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Urban Areas ◽  
Image Features ◽  
Training Data ◽  
Fine Tuning ◽  
Computer Hardware ◽  
Geographical Information ◽  
Training Dataset ◽  
Very High

Abstract. Automatic detection, segmentation and reconstruction of buildings in urban areas from Earth Observation (EO) data are still challenging for many researchers. Roof is one of the most important element in a building model. The three-dimensional geographical information system (3D GIS) applications generally require the roof type and roof geometry for performing various analyses on the models, such as energy efficiency. The conventional segmentation and classification methods are often based on features like corners, edges and line segments. In parallel to the developments in computer hardware and artificial intelligence (AI) methods including deep learning (DL), image features can be extracted automatically. As a DL technique, convolutional neural networks (CNNs) can also be used for image classification tasks, but require large amount of high quality training data for obtaining accurate results. The main aim of this study was to generate a roof type dataset from very high-resolution (10 cm) orthophotos of Cesme, Turkey, and to classify the roof types using a shallow CNN architecture. The training dataset consists 10,000 roof images and their labels. Six roof type classes such as flat, hip, half-hip, gable, pyramid and complex roofs were used for the classification in the study area. The prediction performance of the shallow CNN model used here was compared with the results obtained from the fine-tuning of three well-known pre-trained networks, i.e. VGG-16, EfficientNetB4, ResNet-50. The results show that although our CNN has slightly lower performance expressed with the overall accuracy, it is still acceptable for many applications using sparse data.

scholarly journals Hybrid method for building extraction in vegetation-rich urban areas from very high-resolution satellite imagery

2017 ◽  
Vol 11 (03) ◽  
pp. 1
Author(s):  
Ajith S. Jayasekare ◽  
Rohan Wickramasuriya ◽  
Mohammad-Reza Namazi-Rad ◽  
Pascal Perez ◽  
Gaurav Singh
Keyword(s):  
High Resolution ◽  
Satellite Imagery ◽  
Hybrid Method ◽  
Urban Areas ◽  
Building Extraction ◽  
High Resolution Satellite Imagery ◽  
Very High

scholarly journals Road Extraction from Very High Resolution Images Using Weakly labeled OpenStreetMap Centerline

2019 ◽  
Vol 8 (11) ◽  
pp. 478 ◽  
Author(s):  
Songbing Wu ◽  
Chun Du ◽  
Hao Chen ◽  
Yingxiao Xu ◽  
Ning Guo ◽  
...  
Keyword(s):  
Deep Learning ◽  
High Resolution ◽  
Road Networks ◽  
Semantic Segmentation ◽  
Google Earth ◽  
Training Data ◽  
City Management ◽  
High Quality ◽  
High Resolution Imagery ◽  
Very High

Road networks play a significant role in modern city management. It is necessary to continually extract current road structure, as it changes rapidly with the development of the city. Due to the success of semantic segmentation based on deep learning in the application of computer vision, extracting road networks from VHR (Very High Resolution) imagery becomes a method of updating geographic databases. The major shortcoming of deep learning methods for road networks extraction is that they need a massive amount of high quality pixel-wise training datasets, which is hard to obtain. Meanwhile, a large amount of different types of VGI (volunteer geographic information) data including road centerline has been accumulated in the past few decades. However, most road centerlines in VGI data lack precise width information and, therefore, cannot be directly applied to conventional supervised deep learning models. In this paper, we propose a novel weakly supervised method to extract road networks from VHR images using only the OSM (OpenStreetMap) road centerline as training data instead of high quality pixel-wise road width label. Large amounts of paired Google Earth images and OSM data are used to validate the approach. The results show that the proposed method can extract road networks from the VHR images both accurately and effectively without using pixel-wise road training data.

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