The Experts below are selected from a list of 98916 Experts worldwide ranked by ideXlab platform
Jordi Inglada - One of the best experts on this subject based on the ideXlab platform.
-
fusion approaches for land cover Map Production using high resolution image time series without reference data of the corresponding period
Remote Sensing, 2017Co-Authors: Benjamin Tardy, Jordi Inglada, Julien MichelAbstract:Optical sensor time series images allow one to produce land cover Maps at a large scale. The supervised classification algorithms have been shown to be the best to produce Maps automatically with good accuracy. The main drawback of these methods is the need for reference data, the collection of which can introduce important Production delays. Therefore, the Maps are often available too late for some applications. Domain adaptation methods seem to be efficient for using past data for land cover Map Production. According to this idea, the main goal of this study is to propose several simple past data fusion schemes to override the current land cover Map Production delays. A single classifier approach and three voting rules are considered to produce Maps without reference data of the corresponding period. These four approaches reach an overall accuracy of around 80% with a 17-class nomenclature using Formosat-2 image time series. A study of the impact of the number of past periods used is also done. It shows that the overall accuracy increases with the number of periods used. The proposed methods require at least two or three previous years to be used.
-
operational high resolution land cover Map Production at the country scale using satellite image time series
Remote Sensing, 2017Co-Authors: Jordi Inglada, Marcela Arias, Benjamin Tardy, David Morin, Arthur Vincent, Isabel RodesAbstract:A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic Production of land cover Maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced Maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the Maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover Maps are provided with a confidence Map which gives information at the pixel level about the expected quality of the result.
-
Assessment of an operational system for crop type Map Production using high temporal and spatial resolution satellite optical imagery
Remote Sensing, 2015Co-Authors: Jordi Inglada, Sophie Bontemps, Guadalupe Sepulcre, Marcela Arias, Silvia Valero, Gérard Dedieu, Benjamin Tardy, David Morin, Olivier Hagolle, Pierre DefournyAbstract:Crop area extent estimates and crop type Maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type Maps at the global scale. Five concurrent strategies for automatic crop type Map Production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the Map Production is fully automatic.
Benjamin Tardy - One of the best experts on this subject based on the ideXlab platform.
-
fusion approaches for land cover Map Production using high resolution image time series without reference data of the corresponding period
Remote Sensing, 2017Co-Authors: Benjamin Tardy, Jordi Inglada, Julien MichelAbstract:Optical sensor time series images allow one to produce land cover Maps at a large scale. The supervised classification algorithms have been shown to be the best to produce Maps automatically with good accuracy. The main drawback of these methods is the need for reference data, the collection of which can introduce important Production delays. Therefore, the Maps are often available too late for some applications. Domain adaptation methods seem to be efficient for using past data for land cover Map Production. According to this idea, the main goal of this study is to propose several simple past data fusion schemes to override the current land cover Map Production delays. A single classifier approach and three voting rules are considered to produce Maps without reference data of the corresponding period. These four approaches reach an overall accuracy of around 80% with a 17-class nomenclature using Formosat-2 image time series. A study of the impact of the number of past periods used is also done. It shows that the overall accuracy increases with the number of periods used. The proposed methods require at least two or three previous years to be used.
-
operational high resolution land cover Map Production at the country scale using satellite image time series
Remote Sensing, 2017Co-Authors: Jordi Inglada, Marcela Arias, Benjamin Tardy, David Morin, Arthur Vincent, Isabel RodesAbstract:A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic Production of land cover Maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced Maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the Maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover Maps are provided with a confidence Map which gives information at the pixel level about the expected quality of the result.
-
Assessment of an operational system for crop type Map Production using high temporal and spatial resolution satellite optical imagery
Remote Sensing, 2015Co-Authors: Jordi Inglada, Sophie Bontemps, Guadalupe Sepulcre, Marcela Arias, Silvia Valero, Gérard Dedieu, Benjamin Tardy, David Morin, Olivier Hagolle, Pierre DefournyAbstract:Crop area extent estimates and crop type Maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type Maps at the global scale. Five concurrent strategies for automatic crop type Map Production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the Map Production is fully automatic.
Marcela Arias - One of the best experts on this subject based on the ideXlab platform.
-
operational high resolution land cover Map Production at the country scale using satellite image time series
Remote Sensing, 2017Co-Authors: Jordi Inglada, Marcela Arias, Benjamin Tardy, David Morin, Arthur Vincent, Isabel RodesAbstract:A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic Production of land cover Maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced Maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the Maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover Maps are provided with a confidence Map which gives information at the pixel level about the expected quality of the result.
-
Assessment of an operational system for crop type Map Production using high temporal and spatial resolution satellite optical imagery
Remote Sensing, 2015Co-Authors: Jordi Inglada, Sophie Bontemps, Guadalupe Sepulcre, Marcela Arias, Silvia Valero, Gérard Dedieu, Benjamin Tardy, David Morin, Olivier Hagolle, Pierre DefournyAbstract:Crop area extent estimates and crop type Maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type Maps at the global scale. Five concurrent strategies for automatic crop type Map Production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the Map Production is fully automatic.
David Morin - One of the best experts on this subject based on the ideXlab platform.
-
operational high resolution land cover Map Production at the country scale using satellite image time series
Remote Sensing, 2017Co-Authors: Jordi Inglada, Marcela Arias, Benjamin Tardy, David Morin, Arthur Vincent, Isabel RodesAbstract:A detailed and accurate knowledge of land cover is crucial for many scientific and operational applications, and as such, it has been identified as an Essential Climate Variable. This accurate knowledge needs frequent updates. This paper presents a methodology for the fully automatic Production of land cover Maps at country scale using high resolution optical image time series which is based on supervised classification and uses existing databases as reference data for training and validation. The originality of the approach resides in the use of all available image data, a simple pre-processing step leading to a homogeneous set of acquisition dates over the whole area and the use of a supervised classifier which is robust to errors in the reference data. The produced Maps have a kappa coefficient of 0.86 with 17 land cover classes. The processing is efficient, allowing a fast delivery of the Maps after the acquisition of the image data, does not need expensive field surveys for model calibration and validation, nor human operators for decision making, and uses open and freely available imagery. The land cover Maps are provided with a confidence Map which gives information at the pixel level about the expected quality of the result.
-
Assessment of an operational system for crop type Map Production using high temporal and spatial resolution satellite optical imagery
Remote Sensing, 2015Co-Authors: Jordi Inglada, Sophie Bontemps, Guadalupe Sepulcre, Marcela Arias, Silvia Valero, Gérard Dedieu, Benjamin Tardy, David Morin, Olivier Hagolle, Pierre DefournyAbstract:Crop area extent estimates and crop type Maps provide crucial information for agricultural monitoring and management. Remote sensing imagery in general and, more specifically, high temporal and high spatial resolution data as the ones which will be available with upcoming systems, such as Sentinel-2, constitute a major asset for this kind of application. The goal of this paper is to assess to what extent state-of-the-art supervised classification methods can be applied to high resolution multi-temporal optical imagery to produce accurate crop type Maps at the global scale. Five concurrent strategies for automatic crop type Map Production have been selected and benchmarked using SPOT4 (Take5) and Landsat 8 data over 12 test sites spread all over the globe (four in Europe, four in Africa, two in America and two in Asia). This variety of tests sites allows one to draw conclusions applicable to a wide variety of landscapes and crop systems. The results show that a random forest classifier operating on linearly temporally gap-filled images can achieve overall accuracies above 80% for most sites. Only two sites showed low performances: Madagascar due to the presence of fields smaller than the pixel size and Burkina Faso due to a mix of trees and crops in the fields. The approach is based on supervised machine learning techniques, which need in situ data collection for the training step, but the Map Production is fully automatic.
Jon Wakefield - One of the best experts on this subject based on the ideXlab platform.
-
modeling and presentation of vaccination coverage estimates using data from household surveys
Vaccine, 2021Co-Authors: Tracy Qi Dong, Jon WakefieldAbstract:It is becoming increasingly popular to produce high-resolution Maps of vaccination coverage by fitting Bayesian geostatistical models to data from household surveys. Usually, the surveys adopt a stratified cluster sampling design. We discuss a number of crucial choices with respect to two key aspects of the Map Production process: the acknowledgement of the survey design in modeling, and the appropriate presentation of estimates and their uncertainties. Specifically, we consider the importance of accounting for urban/rural stratification and cluster-level non-spatial excess variation in survey outcomes, when fitting geostatistical models. We also discuss the trade-off between the geographical scale and precision of model-based estimates, and demonstrate visualization methods for Mapping and ranking that emphasize the probabilistic interpretation of results. A novel approach to coverage Map presentation is proposed to allow comparison and control of the overall Map uncertainty. We use measles vaccination coverage in Nigeria as a motivating example and illustrate the different issues using data from the 2018 Nigeria Demographic and Health Survey.