scholarly journals Land Cover Classification using Machine Learning Approaches from High Resolution Images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akhtar Jamil ◽  
Aftab Ahmed Khan ◽  
Alaa Ali Hameed ◽  
Sibghat Sibghat Ullah Bazai
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Learning Approaches ◽  
High Resolution Images

scholarly journals Land Cover Classification using Machine Learning Approaches from High Resolution Images

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Akhtar Jamil ◽  
Aftab Ahmed Khan ◽  
Alaa Ali Hameed ◽  
Sibghat Sibghat Ullah Bazai
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Land Cover ◽  
Land Cover Classification ◽  
Learning Approaches ◽  
High Resolution Images

scholarly journals Leveraging machine learning to produce a cost-effective national building height map of Ireland

2021 ◽  
Author(s):  
Eoghan Keany ◽  
Geoffrey Bessardon ◽  
Emily Gleeson
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
High Resolution ◽  
Land Cover ◽  
Urban Areas ◽  
Cost Effective ◽  
Land Cover Classification ◽  
National Scale ◽  
Building Height ◽  
Height Data

<p>To represent surface thermal, turbulent and humidity exchanges, Numerical Weather Prediction (NWP) systems require a land-cover classification map to calculate sur-face parameters used in surface flux estimation. The latest land-cover classification map used in the HARMONIE-AROME configuration of the shared ALADIN-HIRLAMNWP system for operational weather forecasting is ECOCLIMAP-SG (ECO-SG). The first evaluation of ECO-SG over Ireland suggested that sparse urban areas are underestimated and instead appear as vegetation areas (1). While the work of (2) on land-cover classification helps to correct the horizontal extent of urban areas, the method does not provide information on the vertical characteristics of urban areas. ECO-SG urban classification implicitly includes building heights (3), and any improvement to ECO-SG urban area extent requires a complementary building height dataset.</p><p>Openly accessible building height data at a national scale does not exist for the island of Ireland. This work seeks to address this gap in availability by extrapolating the preexisting localised building height data across the entire island. The study utilises information from both the temporal and spatial dimensions by creating band-wise temporal aggregation statistics from morphological operations, for both the Sentinel-1A/B and Sentinel-2A/B constellations (4). The extrapolation uses building height information from the Copernicus Urban Atlas, which contains regional coverage for Dublin at 10 m x10 m resolution (5). Various regression models were then trained on these aggregated statistics to make pixel-wise building height estimates. These model estimates were then evaluated with an adjusted RMSE metric, with the most accurate model chosen to map the entire country. This method relies solely on freely available satellite imagery and open-source software, providing a cost-effective mapping service at a national scale that can be updated more frequently, unlike expensive once-off private mapping services. Furthermore, this process could be applied by these services to reduce costs by taking a small representative sample and extrapolating the rest of the area. This method can be applied beyond national borders providing a uniform map that does not depends on the different private service practices facilitating the updates of global or continental land-cover information used in NWP.</p><p> </p><p>(1) G. Bessardon and E. Gleeson, “Using the best available physiography to improve weather forecasts for Ireland,” in Challenges in High-Resolution Short Range NWP at European level including forecaster-developer cooperation, European Meteorological Society, 2019.</p><p>(2) E. Walsh, et al., “Using machine learning to produce a very high-resolution land-cover map for Ireland, ” Advances in Science and Research,  (accepted for publication).</p><p>(3) CNRM, "Wiki - ECOCLIMAP-SG" https://opensource.umr-cnrm.fr/projects/ecoclimap-sg/wiki</p><p>(4) D. Frantz, et al., “National-scale mapping of building height using sentinel-1 and sentinel-2 time series,” Remote Sensing of Environment, vol. 252, 2021.</p><p>(5) M. Fitrzyk, et al., “Esa Copernicus sentinel-1 exploitation activities,” in IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium, IEEE, 2019.</p>

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>

scholarly journals Automatic diagnosis and biopsy classification with dynamic Full-Field OCT and machine learning

2021 ◽  
Author(s):  
Olivier Thouvenin ◽  
Jules Scholler ◽  
Diana Mandache ◽  
Marie Christine Mathieu ◽  
Aïcha Ben Lakhdar ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Automatic Segmentation ◽  
Medical Community ◽  
Tissue Preparation ◽  
Learning Approaches ◽  
Automatic Diagnosis ◽  
Full Field ◽  
Tumor Margins ◽  
High Resolution Images

Abstract The adoption of emerging imaging technologies in the medical community is often hampered if they provide a new unfamiliar contrast that requires experience to be interpreted. Here, in order to facilitate such integration, we developed two complementary machine learning approaches, respectively based on feature engineering and on convolutional neural networks (CNN), to perform automatic diagnosis of breast biopsies using dynamic full field optical coherence tomography (D-FF-OCT) microscopy. This new technique provides fast, high resolution images of biopsies with a contrast similar to H&E histology, but without any tissue preparation and alteration. We conducted a pilot study on 51 breast biopsies, and more than 1,000 individual images, and performed standard histology to obtain each biopsy diagnosis. Using our automatic diagnosis algorithms, we obtained an accuracy above 88% at the image level, and above 96% at the biopsy level. Finally, we proposed different strategies to narrow down the spatial scale of the automatic segmentation in order to be able to draw the tumor margins by drawing attention maps with the CNN approach, or by performing high resolution precise annotation of the datasets. Altogether, these results demonstrate the high potential of D-FF-OCT coupled to machine learning to provide a rapid, automatic, and accurate histopathology diagnosis.

Land-cover classification of an intra-urban environment using high-resolution images and object-based image analysis

2012 ◽  
Vol 33 (19) ◽  
pp. 5973-5995 ◽  
Author(s):  
Carolina Moutinho Duque de Pinho ◽  
Leila Maria Garcia Fonseca ◽  
Thales Sehn Korting ◽  
Cláudia Maria de Almeida ◽  
Hermann Johann Heinrich Kux
Keyword(s):  
Image Analysis ◽  
High Resolution ◽  
Land Cover ◽  
Urban Environment ◽  
Land Cover Classification ◽  
Object Based Image Analysis ◽  
Object Based ◽  
High Resolution Images
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