scholarly journals Towards Seamless Validation of Land Cover Data

2018 ◽  
Vol 1 ◽  
pp. 1-6
Author(s):  
Ekaterina Chuprikova ◽  
Lukas Liebel ◽  
Liqiu Meng
Keyword(s):  
Land Cover ◽  
Ground Truth ◽  
Data Sets ◽  
Ground Truth Data ◽  
Know How ◽  
Global Land Cover ◽  
Land Cover Data ◽  
The One ◽  
Crowd Intelligence ◽  
Uncertainty Information

This article demonstrates the ability of the Bayesian Network analysis for the recognition of uncertainty patterns associated with the fusion of various land cover data sets including GlobeLand30, CORINE (CLC2006, Germany) and land cover data derived from Volunteered Geographic Information (VGI) such as Open Street Map (OSM). The results of recognition are expressed as probability and uncertainty maps which can be regarded as a by-product of the GlobeLand30 data. The uncertainty information may guide the quality improvement of GlobeLand30 by involving the ground truth data, information with superior quality, the know-how of experts and the crowd intelligence. Such an endeavor aims to pave a way towards a seamless validation of global land cover data on the one hand and a targeted knowledge discovery in areas with higher uncertainty values on the other hand.

scholarly journals A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1207-1211
Author(s):  
N. Soyama ◽  
K. Muramatsu ◽  
M. Daigo ◽  
F. Ochiai ◽  
N. Fujiwara
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Ground Truth ◽  
Plant Functional Type ◽  
Data Sets ◽  
Functional Type ◽  
Reference Dataset ◽  
Ground Truth Data ◽  
Global Land Cover ◽  
Global Land

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.

scholarly journals A STUDY ON PRODUCING HIGHLY RELIABILE REFERENCE DATA SETS FOR GLOBAL LAND COVER VALIDATION

2016 ◽  
Vol XLI-B8 ◽  
pp. 1207-1211
Author(s):  
N. Soyama ◽  
K. Muramatsu ◽  
M. Daigo ◽  
F. Ochiai ◽  
N. Fujiwara
Keyword(s):  
Land Cover ◽  
Reference Data ◽  
Ground Truth ◽  
Plant Functional Type ◽  
Data Sets ◽  
Functional Type ◽  
Reference Dataset ◽  
Ground Truth Data ◽  
Global Land Cover ◽  
Global Land

Validating the accuracy of land cover products using a reliable reference dataset is an important task. A reliable reference dataset is produced with information derived from ground truth data. Recently, the amount of ground truth data derived from information collected by volunteers has been increasing globally. The acquisition of volunteer-based reference data demonstrates great potential. However information given by volunteers is limited useful vegetation information to produce a complete reference dataset based on the plant functional type (PFT) with five specialized forest classes. In this study, we examined the availability and applicability of FLUXNET information to produce reference data with higher levels of reliability. FLUXNET information was useful especially for forest classes for interpretation in comparison with the reference dataset using information given by volunteers.

scholarly journals A study on landuse and landcover classification using microwave data in Joida taluk of Uttara Kannada district, Karnataka

2020 ◽  
Vol 12 (1) ◽  
pp. 9-12
Author(s):  
Arjun G. Koppad ◽  
Syeda Sarfin ◽  
Anup Kumar Das
Keyword(s):  
Land Use ◽  
Land Cover ◽  
Accuracy Assessment ◽  
Ground Truth ◽  
Land Cover Classification ◽  
Data Sets ◽  
Radiometric Calibration ◽  
Ground Truth Data ◽  
Dense Forest ◽  
Sar Data

The study has been conducted for land use and land cover classification by using SAR data. The study included examining of ALOS 2 PALSAR L- band quad pol (HH, HV, VH and VV) SAR data for LULC classification. The SAR data was pre-processed first which included multilook, radiometric calibration, geometric correction, speckle filtering, SAR Polarimetry and decomposition. For land use land cover classification of ALOS-2-PALSAR data sets, the supervised Random forest classifier was used. Training samples were selected with the help of ground truth data. The area was classified under 7 different classes such as dense forest, moderate dense forest, scrub/sparse forest, plantation, agriculture, water body, and settlements. Among them the highest area was covered by dense forest (108647ha) followed by horticulture plantation (57822 ha) and scrub/Sparse forest (49238 ha) and lowest area was covered by moderate dense forest (11589 ha).   Accuracy assessment was performed after classification. The overall accuracy of SAR data was 80.36% and Kappa Coefficient was 0.76.  Based on SAR backscatter reflectance such as single, double, and volumetric scattering mechanism different land use classes were identified.

scholarly journals Plant functional type classification for earth system models: results from the European Space Agency's Land Cover Climate Change Initiative

2015 ◽  
Vol 8 (7) ◽  
pp. 2315-2328 ◽  
Author(s):  
B. Poulter ◽  
N. MacBean ◽  
A. Hartley ◽  
I. Khlystova ◽  
O. Arino ◽  
...  
Keyword(s):  
Land Cover ◽  
European Space Agency ◽  
Data Sets ◽  
Earth System ◽  
Data Set ◽  
System Models ◽  
Global Land Cover ◽  
Space Agency ◽  
Land Cover Data ◽  
Earth System Models

Abstract. Global land cover is a key variable in the earth system with feedbacks on climate, biodiversity and natural resources. However, global land cover data sets presently fall short of user needs in providing detailed spatial and thematic information that is consistently mapped over time and easily transferable to the requirements of earth system models. In 2009, the European Space Agency launched the Climate Change Initiative (CCI), with land cover (LC_CCI) as 1 of 13 essential climate variables targeted for research development. The LC_CCI was implemented in three phases: first responding to a survey of user needs; developing a global, moderate-resolution land cover data set for three time periods, or epochs (2000, 2005, and 2010); and the last phase resulting in a user tool for converting land cover to plant functional type equivalents. Here we present the results of the LC_CCI project with a focus on the mapping approach used to convert the United Nations Land Cover Classification System to plant functional types (PFTs). The translation was performed as part of consultative process among map producers and users, and resulted in an open-source conversion tool. A comparison with existing PFT maps used by three earth system modeling teams shows significant differences between the LC_CCI PFT data set and those currently used in earth system models with likely consequences for modeling terrestrial biogeochemistry and land–atmosphere interactions. The main difference between the new LC_CCI product and PFT data sets used currently by three different dynamic global vegetation modeling teams is a reduction in high-latitude grassland cover, a reduction in tropical tree cover and an expansion in temperate forest cover in Europe. The LC_CCI tool is flexible for users to modify land cover to PFT conversions and will evolve as phase 2 of the European Space Agency CCI program continues.

scholarly journals Assessing Consistency of Five Global Land Cover Data Sets in China

2014 ◽  
Vol 6 (9) ◽  
pp. 8739-8759 ◽  
Author(s):  
Yan Bai ◽  
Min Feng ◽  
Hao Jiang ◽  
Juanle Wang ◽  
Yunqiang Zhu ◽  
...  
Keyword(s):  
Land Cover ◽  
Data Sets ◽  
Global Land Cover ◽  
Land Cover Data ◽  
Global Land

scholarly journals Deep Learning Based Cardiac MRI Segmentation: Do We Need Experts?

Algorithms ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 212
Author(s):  
Youssef Skandarani ◽  
Pierre-Marc Jodoin ◽  
Alain Lalande
Keyword(s):  
Deep Learning ◽  
Cardiac Mri ◽  
Expert Knowledge ◽  
Medical Image Analysis ◽  
Ground Truth ◽  
Cine Mri ◽  
Data Sets ◽  
Mri Segmentation ◽  
Segmentation Evaluation ◽  
Ground Truth Data

Deep learning methods are the de facto solutions to a multitude of medical image analysis tasks. Cardiac MRI segmentation is one such application, which, like many others, requires a large number of annotated data so that a trained network can generalize well. Unfortunately, the process of having a large number of manually curated images by medical experts is both slow and utterly expensive. In this paper, we set out to explore whether expert knowledge is a strict requirement for the creation of annotated data sets on which machine learning can successfully be trained. To do so, we gauged the performance of three segmentation models, namely U-Net, Attention U-Net, and ENet, trained with different loss functions on expert and non-expert ground truth for cardiac cine–MRI segmentation. Evaluation was done with classic segmentation metrics (Dice index and Hausdorff distance) as well as clinical measurements, such as the ventricular ejection fractions and the myocardial mass. The results reveal that generalization performances of a segmentation neural network trained on non-expert ground truth data is, to all practical purposes, as good as that trained on expert ground truth data, particularly when the non-expert receives a decent level of training, highlighting an opportunity for the efficient and cost-effective creation of annotations for cardiac data sets.

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