Innovative Geospatial and Cartographic Approaches to Identification, Analysis, and Visualisation of Land Degradation
Bibliographic record
Abstract
Abstract. Land degradation has increasingly been recognised as a serious environmental problem throughout the whole world. Six major threats may be identified that place soil fertility at risk, namely, soil erosion, loss of organic matter, soil biodiversity, soil compaction, soil salinity, and soil pollution. Several of those risks could be effectively identified, analysed, visualised and consequently limited by approaches originating from the geospatial domain.The China – European Union research and innovation project “SIEUSOIL” (SIno-EU Soil Observatory for Intelligent Land Use Management), funded between 2019 and 2022, as well as the European Big Data flagship research and innovation project “DataBio” (Data-Driven Bioeconomy), funded between 2017 and 2019, aim at the above mentioned challenges of land degradation from innovative geospatial perspectives.Such innovative approaches include local geological, geomorphological, climate, yield maps as well as global soil maps provided commonly by the European Commission (Directorate General Joint Research Centre) and the Chinese Academy of Sciences. Among the data inputs there are farm machinery tracking and interpreted satellite images. The second level of activities consists of selected standards and specification of ontologies for the integration and sharing of soil related data, facilitating their reuse and understanding. The resulting ontology and other selected models provide the basis for the integration, publication, and querying of the source datasets using Linked data as a federated layer. Besides offering an integrated view of the datasets, this layer also enables new insights through the discovery of links.From the cartographic perspective, several visualization techniques are deployed; from static maps to interactive visualisations. The main focus of visualization techniques is therefore given to the interactivity through utilizing the concept of Multiple Coordinated Views (also known as visual analytics tools) and dynamic queries to emphasize the impact of changes of various phenomena in space and time. For instance, see Figure 1 on monitoring of machinery fleet movement and especially its spatiotemporal changes that can bring new insights into the consequences of human decisions from many areas. Economic reasons are related to economic evidence for a farmer, including fuel consumption, efficiency of trajectory, etc. to revenue authority or subsidies management. On the other hand, ecologic motivations aim to decrease of environmental burden caused e.g. by high CO2 emissions due to a lack of movement optimisation, water pollution by nitrogen due to excessive fertilisation, etc.The outcomes of the above mentioned research are intended as a feedback to (inter)national policies within the environmental domain including the Montreal Protocol (on substances that deplete the Ozone Layer), European Common Agricultural Policy, Nitrates Directive, Air Quality Framework Directive, Water Framework Directive, the Chinese Water Pollution Prevention Law, etc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".