scholarly journals A METHODOLOGY FOR ASSESSING OPENSTREETMAP DEGREE OF COVERAGE FOR PURPOSES OF LAND COVER MAPPING

2015 ◽  
Vol II-3/W5 ◽  
pp. 297-303 ◽  
Author(s):  
A. Ribeiro ◽  
C.C. Fonte
Keyword(s):  
Land Cover ◽  
Region Of Interest ◽  
Land Cover Mapping ◽  
Complete Coverage ◽  
Collaborative Project ◽  
Land Cover Map ◽  
Data Coverage ◽  
Low Levels ◽  
Using Data ◽  
Land Cover Maps

The data available in the collaborative project OpenStreetMap (OSM) is in some locations so detailed and complete that it may provide useful data for Land Cover Map creation and validation. However, this degree of detail is not uniform along space. Therefore, one of the first requirements that needs to be assessed to determine if the creation and validation of Land Cover Maps using data available in OSM may be feasible, is the availability of data to provide a relatively complete coverage of the region of interest. To provide a fast and automatic quantitative assessment of this requirement a methodology is presented and tested in this article. Four study areas are considered, all located in Europe. The results show that the four regions presented very different coverages at the time of data download and its spatial distribution was not uniform. This approach enabled the identification of the most problematic regions for land cover mapping, where low levels of data coverage are available. Since the proposed methodology can be automated, it enables a fast identification of the regions that, in a preliminary analysis, may be considered fit for further analysis to assess fitness for use for Land Cover Map creation and/or validation.

scholarly journals Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering

2018 ◽  
Vol 10 (8) ◽  
pp. 1212 ◽  
Author(s):  
Xiaohong Yang ◽  
Zhong Xie ◽  
Feng Ling ◽  
Xiaodong Li ◽  
Yihang Zhang ◽  
...  
Keyword(s):  
Land Cover ◽  
Spatial Pattern ◽  
Spatial Resolution ◽  
Super Resolution ◽  
Land Cover Mapping ◽  
Fuzzy C Means Clustering ◽  
Land Cover Map ◽  
Spatio Temporal ◽  
Fine Resolution ◽  
Land Cover Maps

Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images.

scholarly journals Toward a Yearly Country-Scale CORINE Land-Cover Map without Using Images: A Map Translation Approach

2021 ◽  
Vol 13 (6) ◽  
pp. 1060
Author(s):  
Luc Baudoux ◽  
Jordi Inglada ◽  
Clément Mallet
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Time Frame ◽  
Superior Performance ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Remote Sensing Images ◽  
Corine Land Cover ◽  
Land Cover Map ◽  
By Products

CORINE Land-Cover (CLC) and its by-products are considered as a reference baseline for land-cover mapping over Europe and subsequent applications. CLC is currently tediously produced each six years from both the visual interpretation and the automatic analysis of a large amount of remote sensing images. Observing that various European countries regularly produce in parallel their own land-cover country-scaled maps with their own specifications, we propose to directly infer CORINE Land-Cover from an existing map, therefore steadily decreasing the updating time-frame. No additional remote sensing image is required. In this paper, we focus more specifically on translating a country-scale remote sensed map, OSO (France), into CORINE Land Cover, in a supervised way. OSO and CLC not only differ in nomenclature but also in spatial resolution. We jointly harmonize both dimensions using a contextual and asymmetrical Convolution Neural Network with positional encoding. We show for various use cases that our method achieves a superior performance than the traditional semantic-based translation approach, achieving an 81% accuracy over all of France, close to the targeted 85% accuracy of CLC.

scholarly journals Improving 3-m Resolution Land Cover Mapping through Efficient Learning from an Imperfect 10-m Resolution Map

2020 ◽  
Vol 12 (9) ◽  
pp. 1418
Author(s):  
Runmin Dong ◽  
Cong Li ◽  
Haohuan Fu ◽  
Jie Wang ◽  
Weijia Li ◽  
...  
Keyword(s):  
Land Cover ◽  
Training Data ◽  
Training Dataset ◽  
Land Cover Mapping ◽  
Remotely Sensed Data ◽  
Large Area ◽  
National Scale ◽  
Substantial Progress ◽  
Efficient Learning ◽  
Land Cover Maps

Substantial progress has been made in the field of large-area land cover mapping as the spatial resolution of remotely sensed data increases. However, a significant amount of human power is still required to label images for training and testing purposes, especially in high-resolution (e.g., 3-m) land cover mapping. In this research, we propose a solution that can produce 3-m resolution land cover maps on a national scale without human efforts being involved. First, using the public 10-m resolution land cover maps as an imperfect training dataset, we propose a deep learning based approach that can effectively transfer the existing knowledge. Then, we improve the efficiency of our method through a network pruning process for national-scale land cover mapping. Our proposed method can take the state-of-the-art 10-m resolution land cover maps (with an accuracy of 81.24% for China) as the training data, enable a transferred learning process that can produce 3-m resolution land cover maps, and further improve the overall accuracy (OA) to 86.34% for China. We present detailed results obtained over three mega cities in China, to demonstrate the effectiveness of our proposed approach for 3-m resolution large-area land cover mapping.

scholarly journals First 1-M Resolution Land Cover Map Labeling the Overlap in the 3rd Dimension: The 2018 Map for Wallonia

Data ◽  
2020 ◽  
Vol 5 (4) ◽  
pp. 117
Author(s):  
Céline Bassine ◽  
Julien Radoux ◽  
Benjamin Beaumont ◽  
Taïs Grippa ◽  
Moritz Lennert ◽  
...  
Keyword(s):  
Land Cover ◽  
Land Use Planning ◽  
Climate Modeling ◽  
Decision Rules ◽  
Ancillary Data ◽  
Map Labeling ◽  
Small Water ◽  
Very High Spatial Resolution ◽  
Land Cover Map ◽  
Land Cover Maps

Land cover maps contribute to a large diversity of geospatial applications, including but not limited to land management, hydrology, land use planning, climate modeling and biodiversity monitoring. In densely populated and highly fragmented landscapes as observed in the Walloon region (Belgium), very high spatial resolution is required to depict all the infrastructures, buildings and most of the structural elements of the semi-natural landscapes (like hedges and small water bodies). Because of the resolution, the vertical dimension needs explicit handling to avoid discontinuities incompatible with many applications. For example, how to map a river flowing under a bridge? The particularity of our data is to provide a two-digit land cover code to label all the overlapping items. The identification of all the overlaps resulted from the combination of remote sensing image analysis and decision rules involving ancillary data. The final product is therefore semantically precise and accurate in terms of land cover description thanks to the addition of 24 classes on top of the 11 pure land cover classes. The quality of the map has been assessed using a state-of-the-art validation scheme. Its overall accuracy is as high as 91.5%, with an average producer’s accuracy of 86% and an average user’s accuracy of 91%.

How to improve 3-m resolution land cover mapping from imperfect 10-m resolution land cover mapping product?

2020 ◽  
Author(s):  
Runmin Dong ◽  
Haohuan Fu
Keyword(s):  
Land Cover ◽  
Large Scale ◽  
Histogram Equalization ◽  
Land Cover Mapping ◽  
Data Set ◽  
Chesapeake Bay Watershed ◽  
Land Cover Map ◽  
Previous State ◽  
Human Interpretation ◽  
Large Scale Land

<p>Land cover mapping has made drastic progress with the improvement of the resolution of remote sensing images in recent research. However, with various limitations of public land cover datasets, human efforts on interpreting and labelling images still account for a significant part of the total cost. For example, it took 10 months and $1.3 million to label about 160,000 square kilometers in the Chesapeake Bay watershed in the northeastern United States. Therefore, it is significant to consider the human interpreting cost of the large-scale land cover mapping.</p><p> </p><p>In this work, we explore a possible solution to achieve 3-m resolution land cover mapping without any human interpretation. This is made possible thanks to a 10-m resolution global land cover map developed for the year of 2017. We propose a complete workflow and a novel deep learning based network to transform the imperfect 10-m resolution land cover map to a preferable 3-m resolution land cover map, which is beneficial to reduce the research thresholds in this community and give similar studies as an example. As we use the imperfect training label, a well-designed and robust approach is strongly needed. We integrate a deep high-resolution network with instance normalization, adaptive histogram equalization, and a pruning process for large-scale land cover mapping.</p><p> </p><p>Our proposed approach achieves the overall accuracy (OA) of 86.83% on the test data set for China, improving the previous state-of-the-art accuracies of 10-m resolution land cover mapping product by 5.35% in OA. Moreover, we present detailed results obtained over three mega cities in China as example and demonstrate the effectiveness of our proposed approach for 3-m resolution large-scale land cover mapping.</p>

scholarly journals Integrating Recent Land Cover Mapping Efforts to Update the National Gap Analysis Program's Species Habitat Map

2014 ◽  
Vol XL-1 ◽  
pp. 245-252 ◽  
Author(s):  
A. J. McKerrow ◽  
A. Davidson ◽  
T. S. Earnhardt ◽  
A. L. Benson
Keyword(s):  
Land Cover ◽  
Gap Analysis ◽  
Google Earth ◽  
Land Cover Mapping ◽  
Suitable Habitat ◽  
Vertebrate Species ◽  
Distribution Models ◽  
Terrestrial Vertebrate ◽  
Landscape Characterization ◽  
Land Cover Map

Over the past decade, great progress has been made to develop national extent land cover mapping products to address natural resource issues. One of the core products of the GAP Program is range-wide species distribution models for nearly 2000 terrestrial vertebrate species in the U.S. We rely on deductive modeling of habitat affinities using these products to create models of habitat availability. That approach requires that we have a thematically rich and ecologically meaningful map legend to support the modeling effort. In this work, we tested the integration of the Multi-Resolution Landscape Characterization Consortium's National Land Cover Database 2011 and LANDFIRE's Disturbance Products to update the 2001 National GAP Vegetation Dataset to reflect 2011 conditions. The revised product can then be used to update the species models. <br><br> We tested the update approach in three geographic areas (Northeast, Southeast, and Interior Northwest). We used the NLCD product to identify areas where the cover type mapped in 2011 was different from what was in the 2001 land cover map. We used Google Earth and ArcGIS base maps as reference imagery in order to label areas identified as "changed" to the appropriate class from our map legend. Areas mapped as urban or water in the 2011 NLCD map that were mapped differently in the 2001 GAP map were accepted without further validation and recoded to the corresponding GAP class. We used LANDFIRE's Disturbance products to identify changes that are the result of recent disturbance and to inform the reassignment of areas to their updated thematic label. We ran species habitat models for three species including Lewis's Woodpecker (<i>Melanerpes lewis</i>) and the White-tailed Jack Rabbit (<i>Lepus townsendii</i>) and Brown Headed nuthatch (<i>Sitta pusilla</i>). For each of three vertebrate species we found important differences in the amount and location of suitable habitat between the 2001 and 2011 habitat maps. Specifically, Brown headed nuthatch habitat in 2011 was &minus;14% of the 2001 modeled habitat, whereas Lewis's Woodpecker increased by 4%. The white-tailed jack rabbit (<i>Lepus townsendii</i>) had a net change of &minus;1% (11% decline, 10% gain). For that species we found the updates related to opening of forest due to burning and regenerating shrubs following harvest to be the locally important main transitions. In the Southeast updates related to timber management and urbanization are locally important.

scholarly journals Application of Full Polarimetric ALOS PALSAR for Land Cover Mapping In Sleman District

PROMINE ◽  
2019 ◽  
Vol 6 (1) ◽  
pp. 33-40
Author(s):  
Like Indrawati
Keyword(s):  
Land Cover ◽  
Research Area ◽  
Rice Fields ◽  
Land Cover Mapping ◽  
Visual Interpretation ◽  
Alos Palsar ◽  
Accuracy Test ◽  
Open Land ◽  
Land Cover Maps

The simplest way to interpret polarimetric imagery for land cover classification is to use visualinterpretation methods. The existence of interpretations key as a tool for visual interpretation becomesimportant when different interpreters can produce different results. The quality of the results of theinterpretation of land cover is then determined by the quality of the interpretation tool, in this case, thekey to the interpretation of land cover. The purpose of this study was to make the key to land coverclass interpretation in the Full Polarimetric ALOS PALSAR image, then the interpretation key wasused for reference in making land cover maps and measuring the accuracy of the results of the visualinterpretation. The image used in this study consisted of HH, VV, HV and VH bands. The location ofthe study was in parts of Sleman District. The analysis is done visually by on-screen digitizing onALOS Palsar composite HH + VV HV + VH HH-HV image, which is then interpreted key. The truetest is done by means of the overall accuracy test and Kappa. Visually, ALOS PALSAR imagery isable to distinguish 12 land cover classes in the research area, namely built land, rice fields, mixedgardens, moorlands, salak garden, grass, forest, shrubs, open land, airports, water bodies and lavawith 83% Overall accuracy, and 78% Kappa accuracy.

scholarly journals The standardization and harmonization of land cover classification systems towards harmonized datasets: A Review

2021 ◽  
Author(s):  
Hui Yang ◽  
Songnian Li ◽  
Jun Chen ◽  
Xiaolu Zhang ◽  
Shishuo Xu
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Classification Systems ◽  
Landscape Patterns ◽  
Future Research ◽  
Land Cover Mapping ◽  
Land Cover Data ◽  
Research Problems ◽  
Future Research Directions ◽  
Land Cover Maps

A number of national, regional and global land cover classification systems have been developed to meet specific user requirements for land cover mapping exercises, independent of scale, nomenclature and quality. However, this variety of land-cover classification systems limits the compatibility and comparability of land cover data. Furthermore, the current lack of interoperability between different land cover datasets, often stemming from incompatible land cover classification systems, makes analysis of multi-source, heterogeneous land cover data for various applications a very difficult task. This paper provides a critical review of the harmonization of land cover classification systems, which facilitates the generation, use and analysis of land cover maps consistently. Harmonization of existing land cover classification systems is essential to improve their cross-comparison and validation for understanding landscape patterns and changes. The paper reviews major land cover classification standards according to different scales, summarizes studies on harmonizing land cover mapping, and discusses some research problems that need to be solved and some future research directions. Keywords: land cover; classification system; standard; harmonization

scholarly journals Operational mapping of the land cover of the forested area of Canada with Landsat data: EOSD land cover program

2003 ◽  
Vol 79 (6) ◽  
pp. 1075-1083 ◽  
Author(s):  
M A Wulder ◽  
J A Dechka ◽  
M A Gillis ◽  
J E Luther ◽  
R J Hall ◽  
...  
Keyword(s):  
Land Cover ◽  
Data Storage ◽  
Land Cover Mapping ◽  
Landsat Data ◽  
Large Area ◽  
Joint Project ◽  
Complete Representation ◽  
Forested Area ◽  
Area Of Interest ◽  
Land Cover Map

A priority of the Canadian Forest Service and Canadian Space Agency joint project, Earth Observation for Sustainable Development of Forests (EOSD), is the production of a land cover map of the forested area of Canada based upon Landsat data. The land cover will be produced through a partnership of federal, provincial and territorial governments, universities, and industry. The short-term goal of EOSD is to complete a land cover map representing year 2000 forested area conditions by early 2006. Over the longer term, EOSD will aim to produce land cover products to capture changes in forest conditions over time to support national and international reporting requirements. The forested area of Canada represents approximately half of Canada's landmass, requiring over 450 scenes for complete coverage (with overlap minimized). EOSD is working with provincial and territorial mapping agencies that have on-going land cover mapping programs to optimize production capacity. It is envisioned that the combined output of EOSD and provincial and territorial land cover mapping programs will be integrated with maps developed by other sectors and agencies (such as agriculture) to produce a complete representation of the land cover of Canada. Large-area land cover mapping using remote sensing is a relatively new phenomenon. Advances in data storage capabilities, computing power, and increases in the affordability of data have allowed for large-area projects to be undertaken in ways previously not possible. The manner in which a large-area mapping project is approached is related to a number of factors including the spatial extent of the area of interest, the spatial resolution of the selected sensor, and the products which are to be generated. In this communication we report on the strategy, methods, and status of the EOSD land cover mapping program of the forested area of Canada. Key words: Canada, land cover, forest inventory, EOSD, Landsat, unsupervised classification, NFI

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