A Systematic Review of the Latest Research Trends on the Use of Satellite Imagery in Solid Waste Disposal Applications from 2012 to 2021
Bibliographic record
Abstract
There is currently no review article on the role of remote sensing (RS) tools on waste disposal site (WDS) applications. Permanent waste disposal is the world’s most commonly used solid waste management method, and a specific review is warranted. To investigate research trends and to identify knowledge gaps on the use of satellite-based RS in WDS applications, 170 studies published over the last decade, from 2012 to 2021, were examined and classified using a bibliometric approach. Results are discussed with respect to relevancy, satellite types, study origins, RS analytical methods, and applications. Out of 72 short-listed studies, 44.4% were carried out in Asia, followed by Europe with 18.0%. Asia is also a leading region in the use of multiple satellite products. Only two satellite products were utilized in African studies. The absence of local satellites could potentially be the reason behind the sole use of global satellite imagery. Globally, Landsat contributed 70.8% of the total studies. Sentinel products represented only 8.3%. About 44% of the studies used various RS indices when addressing WDS-related issues. The majority of studies (56%) applied image classification methods to study changes in land use and land cover. The temporal trend reveals a general increase in the total number of studies, particularly for suitable site detection and disposal-site-induced anomaly detection. This review directly addresses the knowledge management aspect of data-driven solid waste management.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.027 | 0.027 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".