# Source Registry — Solution Assessment (Mode B)

## Source #1
- **Title**: GLEAM: Learning to Match and Explain in Cross-View Geo-Localization
- **Link**: https://arxiv.org/abs/2509.07450
- **Tier**: L1
- **Publication Date**: 2025-09
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Cross-view geo-localization researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Framework for cross-view geo-localization with explainable matching across modalities. Demonstrates that specialized cross-view methods outperform generic feature matchers.

## Source #2
- **Title**: Robust UAV Image Mosaicking Using SIFT and LightGlue (ISPRS 2025)
- **Link**: https://isprs-archives.copernicus.org/articles/XLVIII-2-W11-2025/169/2025/
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV photogrammetry and aerial image processing
- **Research Boundary Match**: ✅ Full match
- **Summary**: SIFT+LightGlue achieves superior spatial consistency and reliability for UAV image mosaicking, including low-texture and high-rotation conditions. SIFT outperforms SuperPoint for rotation-heavy scenarios.

## Source #3
- **Title**: Precise GPS-Denied UAV Self-Positioning via Context-Enhanced Cross-View Geo-Localization (CEUSP)
- **Link**: https://arxiv.org/abs/2502.11408 / https://github.com/eksnew/ceusp
- **Tier**: L1
- **Publication Date**: 2025-02
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: GPS-denied UAV navigation
- **Research Boundary Match**: ⚠️ Partial overlap (urban, not steppe)
- **Summary**: DINOv2-based cross-view matching for UAV self-positioning. State-of-the-art on DenseUAV benchmark. Uses retrieval-based (not feature-matching) approach.

## Source #4
- **Title**: SatLoc Dataset and Hierarchical Adaptive Fusion Framework
- **Link**: https://www.mdpi.com/2072-4292/17/17/3048
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: GNSS-denied UAV navigation
- **Research Boundary Match**: ✅ Full match
- **Summary**: Three-layer architecture: DINOv2 for absolute geo-localization, XFeat for VO, optical flow for velocity. Adaptive fusion with confidence weighting. <15m absolute error on edge hardware.

## Source #5
- **Title**: LightGlue ONNX/TensorRT acceleration blog
- **Link**: https://fabio-sim.github.io/blog/accelerating-lightglue-inference-onnx-runtime-tensorrt/
- **Tier**: L2
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: LightGlue users optimizing inference
- **Research Boundary Match**: ✅ Full match
- **Summary**: LightGlue ONNX achieves 2-4x speedup over PyTorch. FP8 quantization (Ada/Hopper GPUs only) adds 6x more. RTX 2060 does NOT support FP8.

## Source #6
- **Title**: LightGlue-ONNX GitHub repository
- **Link**: https://github.com/fabio-sim/LightGlue-ONNX
- **Tier**: L2
- **Publication Date**: 2024-2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: LightGlue deployment engineers
- **Research Boundary Match**: ✅ Full match
- **Summary**: ONNX export for LightGlue with FlashAttention-2 support. TopK-trick for ~30% speedup. Pre-exported models available.

## Source #7
- **Title**: LightGlue GitHub Issue #64 — Rotation sensitivity
- **Link**: https://github.com/cvg/LightGlue/issues/64
- **Tier**: L4
- **Publication Date**: 2023-2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: LightGlue users
- **Research Boundary Match**: ✅ Full match
- **Summary**: LightGlue (with SuperPoint/DISK) is NOT rotation-invariant. 90° or 180° rotation causes matching failure. Manual rectification needed.

## Source #8
- **Title**: LightGlue GitHub Issue #13 — No-match handling
- **Link**: https://github.com/cvg/LightGlue/issues/13
- **Tier**: L4
- **Publication Date**: 2023
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: LightGlue users
- **Research Boundary Match**: ✅ Full match
- **Summary**: LightGlue lacks explicit training on unmatchable pairs. May produce geometrically meaningless matches instead of rejecting non-overlapping views.

## Source #9
- **Title**: YFS90/GNSS-Denied-UAV-Geolocalization GitHub
- **Link**: https://github.com/yfs90/gnss-denied-uav-geolocalization
- **Tier**: L1
- **Publication Date**: 2024-2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: GPS-denied UAV navigation
- **Research Boundary Match**: ✅ Full match
- **Summary**: <7m MAE using terrain-weighted constraint optimization + 2D-3D geo-registration. Uses DEM data. Validated across 20 complex scenarios. Works with publicly available satellite maps.

## Source #10
- **Title**: Efficient image matching for UAV visual navigation via DALGlue (Scientific Reports 2025)
- **Link**: https://www.nature.com/articles/s41598-025-21602-5
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV visual navigation
- **Research Boundary Match**: ✅ Full match
- **Summary**: 11.8% MMA improvement over LightGlue. Uses dual-tree complex wavelet transform + adaptive spatial feature fusion + linear attention. Designed for UAV dynamic flight.

## Source #11
- **Title**: XFeat: Accelerated Features for Lightweight Image Matching (CVPR 2024)
- **Link**: https://arxiv.org/html/2404.19174v1 / https://github.com/verlab/accelerated_features
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Real-time feature matching applications
- **Research Boundary Match**: ✅ Full match
- **Summary**: 5x faster than SuperPoint. Runs real-time on CPU. Sparse + semi-dense matching. Used by SatLoc-Fusion for VO. 1500+ GitHub stars.

## Source #12
- **Title**: An Oblique-Robust Absolute Visual Localization Method (IEEE TGRS 2024)
- **Link**: https://ieeexplore.ieee.org/iel7/36/10354519/10356107.pdf
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: GPS-denied UAV localization
- **Research Boundary Match**: ✅ Full match
- **Summary**: SE(2)-steerable network for rotation-equivariant features. Handles drastic perspective changes, non-perpendicular camera angles. No additional training for new scenes.

## Source #13
- **Title**: Google Maps Tiles API Usage and Billing
- **Link**: https://developers.google.com/maps/documentation/tile/usage-and-billing
- **Tier**: L1
- **Publication Date**: 2025-2026 (continuously updated)
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Google Maps API users
- **Research Boundary Match**: ✅ Full match
- **Summary**: 100,000 free tile requests/month. Rate limit: 6,000/min, 15,000/day for 2D tiles. $200/month free credit expired Feb 2025. Now pay-as-you-go only.

## Source #14
- **Title**: GTSAM Python API and Factor Graph examples
- **Link**: https://github.com/borglab/gtsam / https://pypi.org/project/gtsam-develop/
- **Tier**: L1
- **Publication Date**: 2025-2026 (v4.2 stable, v4.3a1 dev)
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Robot navigation, SLAM
- **Research Boundary Match**: ✅ Full match
- **Summary**: Python bindings for factor graph optimization. GPSFactor for absolute position constraints. iSAM2 for incremental optimization. Stable v4.2 for production use.

## Source #15
- **Title**: Copernicus DEM documentation
- **Link**: https://documentation.dataspace.copernicus.eu/APIs/SentinelHub/Data/DEM.html
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: DEM data users
- **Research Boundary Match**: ✅ Full match
- **Summary**: Free 30m DEM (GLO-30) covering Ukraine. API access via Sentinel Hub Process API. Registration required.

## Source #16
- **Title**: Homography Decomposition Revisited (IJCV 2025)
- **Link**: https://link.springer.com/article/10.1007/s11263-025-02680-4
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Computer vision researchers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Existing homography decomposition methods can be unstable in certain configurations. Proposes hybrid framework for improved stability.

## Source #17
- **Title**: Sliding window factor graph optimization for visual/inertial navigation (Cambridge 2020)
- **Link**: https://www.cambridge.org/core/services/aop-cambridge-core/content/view/523C7C41D18A8D7C159C59235DF502D0/
- **Tier**: L1
- **Publication Date**: 2020
- **Timeliness Status**: ✅ Currently valid (foundational method)
- **Target Audience**: Navigation system designers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Sliding-window factor graph optimization combines accuracy of graph optimization with efficiency of windowed approach. Superior to separate filtering or full batch optimization.

## Source #18
- **Title**: SuperPoint feature extraction and matching benchmarks
- **Link**: https://preview-www.nature.com/articles/s41598-024-59626-y/tables/3
- **Tier**: L1
- **Publication Date**: 2024
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Feature matching benchmarking
- **Research Boundary Match**: ✅ Full match
- **Summary**: SuperPoint+LightGlue: ~0.36±0.06s per image pair for extraction+matching on GPU. Competitive accuracy for satellite stereo scenarios.

## Source #19
- **Title**: DINOv2-Based UAV Visual Self-Localization in Low-Altitude Urban Environments
- **Link**: https://ui.adsabs.harvard.edu/abs/2025IRAL...10.2080Y/
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: UAV visual localization researchers
- **Research Boundary Match**: ⚠️ Partial overlap (urban, not steppe)
- **Summary**: DINOv2-based method achieves 86.27 R@1 on DenseUAV benchmark for cross-view matching. Integrates global-local feature enhancement.

## Source #20
- **Title**: Mapbox Satellite Tiles and Pricing
- **Link**: https://docs.mapbox.com/data/tilesets/reference/mapbox-satellite/ / https://mapbox.com/pricing
- **Tier**: L1
- **Publication Date**: 2025
- **Timeliness Status**: ✅ Currently valid
- **Target Audience**: Map tile consumers
- **Research Boundary Match**: ✅ Full match
- **Summary**: Mapbox offers satellite tiles up to 0.3m resolution (zoom 16+). 200,000 free vector tile requests/month. Unlimited offline downloads on pay-as-you-go. Multi-provider imagery (Maxar, Landsat, Sentinel).
