As result of the "Global Land Cover Mapping at Finer Resolution" project led by National Geomatics Center of China (NGCC), one of the first global land cover datasets at 30-meters resolution (GlobeLand30) has been produced for the years 2000 and 2010. The first comprehensive accuracy assessment at a national level of these data (excluding some comparisons in China) has been performed on the Italian area by means of a benchmarking with the more detailed land cover datasets available for some Italian regions. The accuracy evaluation was based on the cell-by-cell comparison between Italian maps and the GlobeLand30 in order to obtain the confusion matrix and its derived statistics (overall accuracy, allocation and quantity disagreements, user and producer accuracy), which help to understand the classification quality. This paper illustrates the adopted methodology and procedures for assessing GlobeLand30 and reports the obtained statistics. The analysis has been performed in eight regions across Italy and shows very good results: the comparison of the datasets according to the first level of Corine Land Cover nomenclature highlights overall accuracy values generally higher than 80%.
OpenStreetMap (OSM) is an extraordinarily large and diverse spatial database of the world. Road networks are amongst the most frequently occurring spatial content within the OSM database. These road network representations are usable in many applications. However the quality of these representations can vary between locations. Comparing OSM road networks with authoritative road datasets for a given area or region is an important task in assessing OSM's fitness for use for applications such as routing and navigation. Comparisons such as these can be technically challenging and no software implementation exists which facilitates such comparisons easily and automatically. In this paper we develop and propose a flexible methodology for comparing the geometry of OSM road network data with other road datasets. Quantitative measures for the completeness and spatial accuracy of OSM are computed including the compatibility of OSM road data with other map databases. Our methodology provides users with significant flexibility in how they can adjust the parameterisation to suit their needs. This software implementation is exclusively built on open source software and a significant degree of automation is provided for these comparisons. This software can subsequently be extended and adapted for comparison between OSM and other external road datasets. have shown that while the number of these contributors actually contributing more than a few perfunctory or exploratory edits is in the tens of thousands the project continues to display incredible growth rates in terms of contributors and the volume of spatial data in the global database. OSM is being used as the source of spatial data for many researchers while the entire project ecosystem itself (the community, motivation of OSM volunteers, etc.) has become the source of increased academic research attention (Arsanjani et al. 2015b).There have been many concerns raised about the quality, accuracy and general fitness-for-use and fitness-for-purpose of VGI data (Ali et al. 2014). Indeed OSM is the subject and basis for many of these concerns. The use of OSM as a source of spatial data is often justified by highlighting the very high financial cost of accessing and using spatial data collected and produced by National Mapping Agencies (NMA) and Commercial Mapping Companies (CMC) (Arsanjani et al. 2015a) and the fact that it is often more up-to-date (Goodchild 2007). The situation regarding access to these data has changed in the past number of years. Many NMA and CMC are making some or all of their spatial data products available as Open Data. The availability of these authoritative spatial datasets as Open Data has provided many opportunities for researchers to investigate the quality of VGI data such as OSM against authoritative spatial data from NMA and CMC.Having access to these datasets does not mean that comparisons are easily carried out. Comparing two or more spatial datasets against each other is a challenging geocomputation problem. In the case of comparing a ...
A global reference data set on cropland was collected through a crowdsourcing campaign using the Geo-Wiki crowdsourcing tool. The campaign lasted three weeks, with over 80 participants from around the world reviewing almost 36,000 sample units, focussing on cropland identification. For quality assessment purposes, two additional data sets are provided. The first is a control set of 1,793 sample locations validated by students trained in satellite image interpretation. This data set was used to assess the quality of the crowd as the campaign progressed. The second data set contains 60 expert validations for additional evaluation of the quality of the contributions. All data sets are split into two parts: the first part shows all areas classified as cropland and the second part shows cropland average per location and user. After further processing, the data presented here might be suitable to validate and compare medium and high resolution cropland maps generated using remote sensing. These could also be used to train classification algorithms for developing new maps of land cover and cropland extent.
Climate issues are nowadays one of the most pressing societal challenges, with cities being identified among the landmarks for climate change. This study investigates the effect of urban land cover composition on a relevant climate-related variable, i.e., the air temperature. The analysis exploits different big geo-data sources, namely high-resolution satellite imagery and in-situ air temperature observations, using the city of Milan (Northern Italy) as a case study. Satellite imagery from the Landsat 8, Sentinel-2, and RapidEye missions are used to derive Local Climate Zone (LCZ) maps depicting land cover compositions across the study area. Correlation tests are run to investigate and measure the influence of land cover composition on air temperature. Results show an underlying connection between the two variables by detecting an average temperature offset of about 1.5 ∘ C between heavily urbanized and vegetated urban areas. The approach looks promising in investigating urban climate at a local scale and explaining effects through maps and exploratory graphs, which are valuable tools for urban planners to implement climate change mitigation strategies. The availability of worldwide coverage datasets, as well as the exclusive use of Free and Open Source Software (FOSS), provide the analysis with a potential to be empowered, replicated, and improved.
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