SLVVA: Scalable Land Viability via Vision-Language Architecture
Bibliographic record
Abstract
Digital soil mapping is a process of creating maps of soil properties and their spatial distribution. It plays a vital role in monitoring soil health and promoting sustainable and efficient land use. In the past, environmental data was used to guide the creation of soil property maps. However, the failure to consider the accessibility of locations has led to a bias in the mapping process. In our research, we utilize satellite imagery to evaluate location accessibility, leading to more balanced soil property mapping. We formulate land viability detection and introduce a scalable two-step framework for its detection. Initially, we classify land viability, followed by its segmentation. We leverage Convolutional Neural Networks (CNNs) for classification and a resilient and generalizable vision-language architecture for segmentation. Our most notable results stem from fine-tuning a pre-existing VGGNet for classification and employing a CLIP-based Segmentation method (CLIPSeg) for segmentation. We demonstrate the effectiveness of our approach through extensive experimentation on EuroSAT and OpenEarthMap datasets. Our work is the first to address the challenge of biased sampling in digital soil mapping by incorporating satellite images to assess the accessibility of locations, ensuring a more representative soil property mapping.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".