Sidewalk Extraction on Aerial Images With Deep Learning and Path Planning Algorithm
Bibliographic record
Abstract
High-definition (HD) maps play an important role in autonomous driving by providing vehicles with localization functionality, environmental information, and support its mission and motion planning. In an HD map, road network extraction/annotation is a crucial feature that helps the autonomous vehicle navigate and keeps it within the safe driving zone. While road network extraction traditionally focuses on motorways and their boundaries, the emergence of small-scale autonomous vehicles, such as delivery and service robots, has shifted attention to sidewalks. Sidewalks are critical for safe and efficient navigation in residential and urban areas, yet automated methods for sidewalk extraction remain underexplored. To address this gap, this article proposes a sidewalk extraction method on aerial images using deep learning with the transfer learning technique. A path-planning algorithm-based refinement method is also proposed to further refine the extracted sidewalk. The proposed method can precisely extract sidewalks from aerial photographs and fix sidewalk discontinuity issues caused by occlusions. A sidewalk dataset is also explicitly designed for sidewalk extraction and semantic segmentation research. This article’s work is meant to fill the sidewalk extraction gap for road network extraction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".