Trailblazer: Learning offroad costmaps for long range planning

Felix Sanchez; Jason M. Gregory; Kasi Viswanath; Srikanth Saripalli; Timothy Overbye

REVIEW 4 major objections 5 minor 1 cited by

Trailblazer: Learning offroad costmaps for long range planning

T0 review · 4 major / 5 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read Trailblazer learns costmaps directly from overhead satellite imagery and LiDAR, so long-range off-road planning no longer needs hand-tuned cost functions.

desk verdict Trailblazer combines Neural A* with overhead multi-modal inputs for off-road global costmap learning, a useful idea that is undercut by the absence of real navigation metrics and inconsistent quantitative reporting. read the letter →

arxiv 2505.09739 v2 pith:6JA5WITR submitted 2025-05-14 cs.RO

Kasi Viswanath , Felix Sanchez , Timothy Overbye , Jason M. Gregory , Srikanth Saripalli This is my paper · ORCID

classification cs.RO

keywords off-roadnavigationcostmaplearningimitationdifferentiableA*long-rangeplanningsatelliteimageryLiDARsim-to-realtransfer

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

Trailblazer is an imitation-learning framework that turns overhead satellite or aerial imagery and LiDAR (or elevation) data directly into costmaps for long-range off-road planning, removing the need for hand-tuned cost functions. The paper claims this can be done end-to-end: a convolutional encoder-decoder produces a costmap from four input maps — semantic segmentation, height, slope, and LiDAR intensity — and a differentiable A* planner traces paths through it, so a simple path-level loss trains the whole system from expert trajectories. Training starts in simulation, then the model is retrained on real-world GPS trajectories to generalize across regions. The authors report real-world deployments in dynamic off-road environments, arguing that publicly available overhead data and GPS traces are sufficient for scalable global planning.

What carries the argument

The load-bearing mechanism is the pairing of a costmap-generating encoder-decoder with a differentiable A* planner. A* is normally a discrete search; here its node-expansion decisions are softened so that every search step can pass a gradient back to the convolutional network, letting the planner's path-level error reshape pixel-level costs. The input representation is also central: four aligned overhead maps (semantic class, average height, slope, and average LiDAR intensity) carry all the terrain information, so the learned cost is a function of interpretable surface properties rather than raw pixels.

What would settle it

Take a held-out off-road site with known hazard locations from a ground survey, retrain Trailblazer on public GPS traces that mostly follow maintained roads, and check whether its planned route crosses hazards that a local expert would avoid; a single such crossing, or a costmap that prices a known hazard below open terrain, would refute the claim that the demonstrations teach safe off-road cost.

Watch

Extended reading notes

Core claim

On the paper's own terms, the discovery is that a global costmap for off-road navigation can be learned rather than designed: given expert demonstrations of good routes, Trailblazer learns which terrain characteristics — semantic class, height, slope, reflectivity — should make a cell expensive or cheap to traverse. The costmap is optimized by the differentiable A* planner, which propagates the difference between the planned path and the demonstrated path back into the cost values. The same architecture accepts either airborne LiDAR or digital elevation maps as geometric input, with DEM quality depending on resolution, and the global costmap can be updated from onboard sensing when local conditions contradict the overhead data.

Load-bearing premise

The load-bearing assumption is that the public GPS trajectories used for retraining are true expert demonstrations of safe, efficient off-road driving; if most of those tracks follow maintained roads or reflect arbitrary driver choices, the learned costmap will encode the wrong notion of traversability.

Editorial extensions

If this is right

Off-road global planning can be carried out from overhead data alone, without a dedicated UAV overflight or hand-tuned cost functions.
Digital elevation maps can replace airborne LiDAR when their resolution is high enough (at least roughly 1/9 arc-second), extending the method to regions without LiDAR coverage.
The global costmap can be updated in real time from onboard sensing, letting the planner override stale overhead cues such as a riverbed that looks dry but is not traversable.
Retraining on crowd-sourced GPS trajectories lets a single model transfer across geographic regions with no additional manual labeling.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

The GPS-demonstration bias is directly testable: retrain the model on tracks that exclusively follow paved roads and compare costmaps; if pavement systematically drops in cost, filtering or reweighting the demonstration set by road class should remove the bias.
The same recipe of overhead surface maps plus a differentiable planner could transfer to other domains with overhead imagery and expert tracks, such as marine surface vehicles or planetary rovers.
Because the loss is path-level rather than pixel-level, the framework could later be combined with self-supervised traversability signals from the robot's own sensors to correct outdated overhead data.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

The one thing to know: Trailblazer is a new and plausible combination—Neural A*, trained end-to-end on imitation trajectories, applied to overhead satellite, LiDAR, and DEM inputs for global off-road costmaps. That combination isn't in the prior literature, and the open code is a plus. But the paper's central claim—robust real-world performance—is not backed by the numbers it reports. The reader's REJECT verdict is, in my view, fair.

What's good: The architecture is cleanly described. Feeding SegFormer superclasses plus height/slope/intensity into a differentiable A* planner and training with L1 path loss is a sensible pipeline. The move from hand-tuned cost functions to learned ones is a real engineering goal. The paper also shows DEMs can replace LiDAR when resolution is sufficient, which is practically useful. The public repo helps reproducibility.

Where it falls short: The evaluation is almost entirely loss values. Section 3.2 gives a validation loss of 0.0256 and a test loss of 0.0213 on trajectory prediction. That is an in-objective measure—same L1 loss used for training—so it tells us little about whether the robot actually navigates well. Real-world tests are mentioned (North Carolina, DARPA RACER) but with no number of trials, distances, success rates, or comparisons against hand-tuned or other learned costmaps. The qualitative "Experimental Insights" section doesn't fill that gap.

There's also a concrete inconsistency: Table 1 gives a mean IoU of 71.69, but the text says "mIoU of 87.5% and an accuracy of 71.69%." One of those is wrong. That sort of slip makes it hard to trust the other numbers.

And the OSM trajectories used as expert demonstrations worry me: those tracks often follow maintained roads or arbitrary driver choices, not the "safe, efficient off-road behavior" you'd want a costmap to mimic. If that assumption is off, the learned costmap inherits it.

None of this means the method can't work. It means the paper, as written, does not substantiate its headline claim. I'd like to see a revision with actual navigation metrics—trial counts, success rates, plan distances, and at least one baseline—plus a fixed Table 1 and a discussion of the OSM demonstration assumption. With that, this could be a useful contribution.

For who: researchers working on learned traversability costmaps and long-range off-road planning. It's probably a workshop-short-paper level as is, but the core idea deserves a serious referee. I'd accept it for review (not desk reject), with the expectation of major revision.

Referee Report

4 major / 5 minor

Summary. The paper presents Trailblazer, an imitation-learning framework that converts overhead multi-modal data (satellite imagery, LiDAR, and DEM-derived inputs) into costmaps for long-range off-road planning. The architecture combines an encoder-decoder with a differentiable Neural A* planner and is trained with an L1 loss between the planner's search history and expert GPS/OSM trajectories. The authors report semantic segmentation results using a SegFormer model trained on FLAIR-One, training and validation loss curves for Trailblazer, a few qualitative costmap/path figures, and a short narrative about real-world experiments conducted under the DARPA RACER program. The central claim is that the methodology was 'validated through extensive real-world testing, achieving robust performance in dynamic and complex environments,' but the only quantitative results reported are segmentation IoU numbers and path-loss values.

Significance. If the central claim were substantiated, Trailblazer would be a useful contribution to off-road navigation: automated costmap generation from publicly available overhead data without manual tuning could reduce deployment effort and improve scalability. The paper also provides a clean end-to-end formulation, uses open datasets, and releases code. However, as submitted, the significance cannot be assessed because the empirical evidence does not support the claimed real-world robustness. The reported training/validation losses are in-objective measures, the field experiments are described only qualitatively, and no baseline comparisons are provided. The contribution is therefore promising but currently unvalidated at the level that a journal publication requires.

major comments (4)

[§3.2, Real-world Experiments; Abstract] The central claim of robust real-world performance is not supported by the reported evidence. The only quantitative results given for Trailblazer are a minimum validation loss of 0.0256 and a test loss of 0.0213, together with a qualitative costmap/path figure. The paragraph on DARPA RACER experiments states that real-time experiments were conducted but provides no number of trials, distance traveled, success rate, replanning rate, runtime, or comparison against hand-tuned or other learned costmaps. Section 4's 'Experimental Insights' are assertions such as 'comparable quality' and 'cross-terrain adaptability' without measurements. Without navigation metrics, the abstract's statement that the methodology 'was validated through extensive real-world testing, achieving robust performance' cannot be assessed.
[§2.2 and §3.2, loss function and test loss] The evaluation is conducted with the same mean L1 path loss used for training, which measures agreement between Neural A*'s search history and ground-truth trajectories. A low test loss indicates that the model can imitate the demonstrations, but it does not establish that the learned costmap is safe, efficient, or suitable for navigation. A planner that always reproduces training-style paths would score well on this metric while producing poor costmaps. An independent evaluation is needed, for example quantitative comparison of planned paths against hand-labeled traversable routes or, preferably, field navigation metrics such as completion rate, distance, and number of interventions.
[Table 1 and §3.1] The quantitative reporting is internally inconsistent. Table 1 reports a mean IoU (mIoU) of 71.69, while §3.1 states that the model 'achieved a mIoU of 87.5% and an accuracy of 71.69% on the validation set.' These numbers are not merely different; they suggest a mix-up between mIoU and accuracy. This inconsistency undermines confidence in the segmentation results and, more broadly, in the reliability of the numerical claims in the paper. The authors must correct the terminology and ensure that every reported number is reproducible from a stated evaluation protocol.
[§3.2, OpenStreetMap trajectories] OSM GPS trajectories are treated as expert demonstrations of good off-road driving without any justification. Section 3.2 says these trajectories are used to retrain Trailblazer for generalization, but if the OSM tracks mostly follow maintained roads or are generated by arbitrary driver behavior, the learned costmap will encode a road-following bias rather than off-road traversability. The paper should analyze the spatial distribution of the OSM data used, report how many trajectories were collected and in what terrain types, and justify why these trajectories are appropriate expert demonstrations for the off-road navigation task.

minor comments (5)

[§4] There is a typo in 'capbility'; it should be 'capability.'
[§2.2] The heading 'T railblazer Architecture' has an erroneous space; it should be 'Trailblazer Architecture.'
[Figure 5 caption] The caption 'Costmap, paths extracted from costmap and OpenStreetmaps tracks from a test site' is vague; it should specify which panel shows which data and what the reader should conclude from the figure.
[§4, coordinate datum discussion] The warning about coordinate transformations between datums is better placed in a 'practical considerations' paragraph; as written, it interrupts the flow of experimental insights and provides no quantitative assessment of misalignment effects.
[Reference [3]] Reference [3] lists only the paper title; author names and publication venue should be included.

Circularity Check

0 steps flagged · score 0.0 of 10

No circularity: reported losses are standard held-out evaluation; the robustness claim is under-supported but not circular.

full rationale

No load-bearing circular step is present. Trailblazer is an end-to-end imitation-learning pipeline: the costmap is generated by an encoder-decoder and Neural A* planner, and the training loss is the mean L1 distance between Neural A* search history and ground-truth trajectories (Sec. 2.2: 'The loss function used is the mean L1 loss between the search history and the ground-truth path'). The reported 'test loss of 0.0213' (Sec. 3.2) is a held-out evaluation of that same objective. Reporting held-out loss of the training objective is standard supervised-learning practice; it does not make the prediction equivalent to the input by construction, because the model is evaluated on data not used for fitting and the loss does not enter the forward computation. The only self-citation, [17], is historical context ('Trailblazer started as a local planning algorithm [17]') and is not used to justify the central claim. External components (SegFormer, Neural A*, MAVS, FLAIR-one, USGS/ESRI data) supply the actual machinery. The main weaknesses are evidential, not circular: the abstract's 'extensive real-world testing' claim is supported only by loss curves and qualitative figures, not by field navigation metrics, and the reported mIoU values are internally inconsistent (87.5% vs 71.69%). Those are support/correctness issues, not cases where a result reduces to its own input. Score 0.

Assumptions & free parameters 3 free parameters · 5 assumptions · 0 invented entities

Trailblazer introduces no new physical entities or forces. Its central claim rests on domain assumptions about the quality of demonstration data, the sufficiency of overhead sensing, and the transferability of simulation training, plus hand-chosen semantic risk orderings and grid resolutions.

free parameters (3)

Semantic superclass risk ordering = ascending from traversable to water
The paper maps five semantic superclasses to risk values by hand; this ordering directly shapes the costmap learning target.
Grid cell resolution and map inputs = not specified (30 cm/pixel imagery, 8 pts/m2 LiDAR)
All input maps are rasterized at a chosen resolution; the paper does not report the costmap grid size or how resolution was selected.
DEM resolution threshold = 1/9 arc-second
The paper states that DEMs below 1/9 arc-second produce subpar costmaps, a hand-chosen cutoff that is not derived from any quantitative analysis.

assumptions (5)

domain assumption OpenStreetMap GPS trajectories are expert demonstrations of good off-road driving
Section 3.2 uses OSM trajectories to retrain Trailblazer; if these tracks are not good examples, the learned costmap will be wrong.
domain assumption MAVS simulation data transfers to real-world off-road navigation
The model is initially trained on MAVS simulation data and then evaluated on real-world sites; sim-to-real transfer is assumed to hold.
domain assumption SegFormer superclass grouping into five risk classes is sufficient for traversability
Section 3.1 consolidates 19 semantic classes into 5 superclasses; this grouping is not validated against actual driving outcomes.
domain assumption Overhead data (satellite, LiDAR/DEM) provides sufficient information for traversability
The entire framework assumes that overhead inputs capture the terrain properties that determine safe driving; dynamic ground conditions may not be visible from above.
domain assumption Neural A* gradients are meaningful for learning costmaps
The differentiable A* module is taken from prior work and assumed to provide useful learning signal when combined with the encoder-decoder.

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Cite this review

Pith. "Pith review of Trailblazer: Learning offroad costmaps for long range planning." pith.science (2026). https://pith.science/paper/6JA5WITR

@misc{pith2026250509739,
  author       = {Pith},
  title        = {Pith review of: Trailblazer: Learning offroad costmaps for long range planning},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/6JA5WITR}},
  note         = {Machine review of arXiv:2505.09739}
}

read the original abstract

Autonomous navigation in off-road environments remains a significant challenge in field robotics, particularly for Unmanned Ground Vehicles (UGVs) tasked with search and rescue, exploration, and surveillance. Effective long-range planning relies on the integration of onboard perception systems with prior environmental knowledge, such as satellite imagery and LiDAR data. This work introduces Trailblazer, a novel framework that automates the conversion of multi-modal sensor data into costmaps, enabling efficient path planning without manual tuning. Unlike traditional approaches, Trailblazer leverages imitation learning and a differentiable A* planner to learn costmaps directly from expert demonstrations, enhancing adaptability across diverse terrains. The proposed methodology was validated through extensive real-world testing, achieving robust performance in dynamic and complex environments, demonstrating Trailblazer's potential for scalable, efficient autonomous navigation.

Figures

Figures reproduced from arXiv: 2505.09739 by the authors.

**Figure 1.** Inputs to Trailblazer from Texas A&M RELLIS test site. The satellite image is of 30 cmperpixel resolution used to generate semantic map from SegFormer. Overhead LiDAR data is used to generate the height, slope and intensity maps. The RELLIS test site covers an area of 1.5km ∗ 1.5km. pute four maps as input for Trailblazer. The four maps consists of (i) Semantic segmentation, (ii) Height map, (iii) Slope profile (iv)… view at source ↗

**Figure 2.** Trailblazer framework architecture. The backbone consists of feature fusion and feature extraction branch generating the costmap. The Neural A* planner generates the path from the costmap compared against ground truth to compute loss. a convolutional encoder to form an end-to-end trainable neural network planner. The differentiable A* module employs techniques like discretized activation inspired by Hubara et al. [… view at source ↗

**Figure 3.** Semantic ground truths and predictions from Segformer [PITH_FULL_IMAGE:figures/full_fig_p006_3.png] view at source ↗

Figures from the paper (2 more)

**Figure 5.** Figure 5: Costmap, paths extracted from costmap and OpenStreetmaps tracks from a test site [PITH_FULL_IMAGE:figures/full_fig_p006_5.png]

**Figure 6.** Figure 6: LiDAR vs DEM: (a) Satellite image from a test site (b) Costmap generated from LiDAR data with 4 points/m2 resolution., (c) Costmap generated from DEM with a 1/3arc − sec resolution. 4 Experimental Insights Traditional path planning relied on manual data collection via …

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

Learning Traversability-Aware Global Planners for Long Horizon Off-Road Navigation
cs.RO 2026-07 conditional novelty 6.0 of 10

Overhead multi-modal learning with PU human-trajectory supervision and LiDAR priors yields global off-road costmaps that nearly match human path length and sharply cut interventions versus local planners.

Reference graph

Works this paper leans on

17 extracted references · 16 canonical work pages · cited by 1 Pith paper

[1]

In: Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems

Vandapel, N., Donamukkala, R.R., Hebert, M.: Quality assessment of traversability maps from aerial lidar data for an unmanned ground vehicle. In: Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 1, pp. 305 – 310 (October 2003)

work page 2003
[2]

Machines 11(8) (2023)

S´ anchez, M., Morales, J., Mart´ ınez, J.L.: Waypoint generation in satellite images based on a cnn for outdoor ugv navigation. Machines 11(8) (2023)

work page 2023
[3]

262–269 (2009)

High Performance Outdoor Navigation from Overhead Data using Imitation Learn- ing, pp. 262–269 (2009)

work page 2009
[4]

Guaman Castro, M., Triest, S., Wang, W., Gregory, J.M., Sanchez, F., Rogers III, J.G., Scherer, S.: How does it feel? self-supervised costmap learning for off-road vehicle traversability (2023)

work page 2023
[5]

In: 2022 International Conference on Robotics and Automation (ICRA)

Weerakoon, K., Sathyamoorthy, A.J., Patel, U., Manocha, D.: Terp: Reliable plan- ning in uneven outdoor environments using deep reinforcement learning. In: 2022 International Conference on Robotics and Automation (ICRA). pp. 9447–9453 Trailblazer: Learning off-road costmaps for long range planning 9

work page 2022
[6]

In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)

Huang, C., Mees, O., Zeng, A., Burgard, W.: Visual language maps for robot navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). London, UK (2023)

work page 2023
[7]

Geological Survey: USGS Lidar Explorer (2025), https://apps.nationalmap

U.S. Geological Survey: USGS Lidar Explorer (2025), https://apps.nationalmap. gov/lidar-explorer/

work page 2025
[8]

Available at: https: //www.esri.com/arcgis

Esri: Arcgis geographic information system (2025), version 11.3. Available at: https: //www.esri.com/arcgis

work page 2025

Show all 17 references

[9]

In: Neural Information Processing Systems (NeurIPS) (2021)

Xie, E., Wang, W., Yu, Z., Anandkumar, A., Alvarez, J.M., Luo, P.: Segformer: Simple and efficient design for semantic segmentation with transformers. In: Neural Information Processing Systems (NeurIPS) (2021)

2021
[10]

12979.pdf

Garioud, A., Peillet, S., Bookjans, E., Giordano, S., Wattrelos, B.: Flair 1: semantic segmentation and domain adaptation dataset (2022), https://arxiv.org/pdf/2211. 12979.pdf

2022
[11]

The International Journal of Robotics Research 36(10), 1073–1087 (2017)

Wulfmeier, M., Rao, D., Wang, D.Z., Ondruska, P., Posner, I.: Large-scale cost function learning for path planning using deep inverse reinforcement learning. The International Journal of Robotics Research 36(10), 1073–1087 (2017)

2017
[12]

In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y

Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: Cbam: Convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. pp. 3–19. Springer International Publishing, Cham (2018)

2018
[13]

In: Meila, M., Zhang, T

Yonetani, R., Taniai, T., Barekatain, M., Nishimura, M., Kanezaki, A.: Path plan- ning using neural a* search. In: Meila, M., Zhang, T. (eds.) Proceedings of the 38th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 139, pp. 12029–12...

2021
[14]

In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R

Hubara, I., Courbariaux, M., Soudry, D., El-Yaniv, R., Bengio, Y.: Binarized neural networks. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 29 (2016)

2016
[15]

In: Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium

Hudson, C., Goodin, C., Miller, Z., Wheeler, W., Carruth, D.: Mississippi state university autonomous vehicle simulation library. In: Proceedings of the Ground Vehicle Systems Engineering and Technology Symposium. pp. 11–13 (2020)

2020
[16]

https://www.openstreetmap.org (2017)

OpenStreetMap contributors: Planet dump retrieved from https://planet.osm.org . https://www.openstreetmap.org (2017)

2017
[17]

Vishwanath, K., Sujit, P.B., Saripalli, S.: Camel: Learning cost-maps made easy for off-road driving (2022), https://arxiv.org/abs/2209.12413

2022 arXiv

Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.

[1] [1]

In: Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems

Vandapel, N., Donamukkala, R.R., Hebert, M.: Quality assessment of traversability maps from aerial lidar data for an unmanned ground vehicle. In: Proceedings of (IROS) IEEE/RSJ International Conference on Intelligent Robots and Systems. vol. 1, pp. 305 – 310 (October 2003)

work page 2003

[2] [2]

Machines 11(8) (2023)

S´ anchez, M., Morales, J., Mart´ ınez, J.L.: Waypoint generation in satellite images based on a cnn for outdoor ugv navigation. Machines 11(8) (2023)

work page 2023

[3] [3]

262–269 (2009)

High Performance Outdoor Navigation from Overhead Data using Imitation Learn- ing, pp. 262–269 (2009)

work page 2009

[4] [4]

Guaman Castro, M., Triest, S., Wang, W., Gregory, J.M., Sanchez, F., Rogers III, J.G., Scherer, S.: How does it feel? self-supervised costmap learning for off-road vehicle traversability (2023)

work page 2023

[5] [5]

In: 2022 International Conference on Robotics and Automation (ICRA)

Weerakoon, K., Sathyamoorthy, A.J., Patel, U., Manocha, D.: Terp: Reliable plan- ning in uneven outdoor environments using deep reinforcement learning. In: 2022 International Conference on Robotics and Automation (ICRA). pp. 9447–9453 Trailblazer: Learning off-road costmaps for long range planning 9

work page 2022

[6] [6]

In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA)

Huang, C., Mees, O., Zeng, A., Burgard, W.: Visual language maps for robot navigation. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA). London, UK (2023)

work page 2023

[7] [7]

Geological Survey: USGS Lidar Explorer (2025), https://apps.nationalmap

U.S. Geological Survey: USGS Lidar Explorer (2025), https://apps.nationalmap. gov/lidar-explorer/

work page 2025

[8] [8]

Available at: https: //www.esri.com/arcgis

Esri: Arcgis geographic information system (2025), version 11.3. Available at: https: //www.esri.com/arcgis

work page 2025