Automatic Extraction and Filtering of OpenStreetMap Data to Generate Training Datasets for Land Use Land Cover Classification

Landsat-derived impervious surface area expansion in the Arctic from 1985 to 2021

Accurate and timely impervious surface mapping is essential for assessing land cover change, urban heat island, and monitoring human activity intensity and ecological change. While various global impervious surface datasets become available, these datasets exhibit significant omissions in Arctic regions. Hence, in this study, we present a 30-m impervious surface area (ISA) dataset of Arctic from 1985 to 2021 (GISA_Arcitc). To this aim, we proposed to combine visually interpreted ISA samples and automatically generated NonISA samples for Arctic ISA mapping. Then, adaptive random forest (RF) classifiers were used for long time-series ISA mapping and the result was post-processed to improve the spatial-temporal consistency. Finally, the accuracy of GISA_Arcitc was assessed using the 37,800 independent test samples. GISA_Arctic possessed an overall accuracy of 93.59 % and a F-score of 0.934. It is found that the Arctic ISA increased from 857.83 km2 to 2115.49 km2 during the past 37 years. More than 84 % of the Arctic ISA increment is embraced by three countries: Russia, Finland, and Norway. Courtesy of the long time-series GISA_Arctic, the sources of Arctic ISA expansion were further analyzed. It was found that the top three land covers transformed to ISA are tundra, forest and grassland. The GISA_Arctic could contribute to further understanding of human activities and Arctic ecological changes, which can be accessed from http://irsip.whu.edu.cn/resources/resources_v2.php.

Predicting building types using OpenStreetMap

Having accurate building information is paramount for a plethora of applications, including humanitarian efforts, city planning, scientific studies, and navigation systems. While volunteered geographic information from sources such as OpenStreetMap (OSM) has good building geometry coverage, descriptive attributes such as the type of a building are sparse. To fill this gap, this study proposes a supervised learning-based approach to provide meaningful, semantic information for OSM data without manual intervention. We present a basic demonstration of our approach that classifies buildings into either residential or non-residential types for three study areas: Fairfax County in Virginia (VA), Mecklenburg County in North Carolina (NC), and the City of Boulder in Colorado (CO). The model leverages (i) available OSM tags capturing non-spatial attributes, (ii) geometric and topological properties of the building footprints including adjacent types of roads, proximity to parking lots, and building size. The model is trained and tested using ground truth data available for the three study areas. The results show that our approach achieves high accuracy in predicting building types for the selected areas. Additionally, a trained model is transferable with high accuracy to other regions where ground truth data is unavailable. The OSM and data science community are invited to build upon our approach to further enrich the volunteered geographic information in an automated manner.

Spatially Stratified and Multi-Stage Approach for National Land Cover Mapping Based on Sentinel-2 Data and Expert Knowledge

Portugal is building a land cover monitoring system to deliver land cover products annually for its mainland territory. This paper presents the methodology developed to produce a prototype relative to 2018 as the first land cover map of the future annual map series (COSsim). A total of thirteen land cover classes are represented, including the most important tree species in Portugal. The mapping approach developed includes two levels of spatial stratification based on landscape dynamics. Strata are analysed independently at the higher level, while nested sublevels can share data and procedures. Multiple stages of analysis are implemented in which subsequent stages improve the outputs of precedent stages. The goal is to adjust mapping to the local landscape and tackle specific problems or divide complex mapping tasks in several parts. Supervised classification of Sentinel-2 time series and post-classification analysis with expert knowledge were performed throughout four stages. The overall accuracy of the map is estimated at 81.3% (±2.1) at the 95% confidence level. Higher thematic accuracy was achieved in southern Portugal, and expert knowledge significantly improved the quality of the map.

Data Fusion in Earth Observation and the Role of Citizen as a Sensor: A Scoping Review of Applications, Methods and Future Trends

Recent advances in Earth Observation (EO) placed Citizen Science (CS) in the highest position, declaring their essential provision of information in every discipline that serves the SDGs, and the 2050 climate neutrality targets. However, so far, none of the published literature reviews has investigated the models and tools that assimilate these data sources. Following this gap of knowledge, we synthesised this scoping systematic literature review (SSLR) with a will to cover this limitation and highlight the benefits and the future directions that remain uncovered. Adopting the SSLR guidelines, a double and two-level screening hybrid process found 66 articles to meet the eligibility criteria, presenting methods, where data were fused and evaluated regarding their performance, scalability level and computational efficiency. Subsequent reference is given on EO-data, their corresponding conversions, the citizens’ participation digital tools, and Data Fusion (DF) models that are predominately exploited. Preliminary results showcased a preference in the multispectral satellite sensors, with the microwave sensors to be used as a supplementary data source. Approaches such as the “brute-force approach” and the super-resolution models indicate an effective way to overcome the spatio-temporal gaps and the so far reliance on commercial satellite sensors. Passive crowdsensing observations are foreseen to gain a greater audience as, described in, most cases as a low-cost and easily applicable solution even in the unprecedented COVID-19 pandemic. Immersive platforms and decentralised systems should have a vital role in citizens’ engagement and training process. Reviewing the DF models, the majority of the selected articles followed a data-driven method with the traditional algorithms to still hold significant attention. An exception is revealed in the smaller-scale studies, which showed a preference for deep learning models. Several studies enhanced their methods with the active-, and transfer-learning approaches, constructing a scalable model. In the end, we strongly support that the interaction with citizens is of paramount importance to achieve a climate-neutral Earth.