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3D Drone Model of Charming T2 In A Green Setting Air Conditioned Bungalow With Stunning Sea View One Bedroom Apartment in Sainte Luce, Le Marin, Martinique

Name: 3D Drone Model of Charming T2 In A Green Setting Air Conditioned Bungalow With Stunning Sea View One Bedroom Apartment in Sainte Luce, Le Marin, Martinique
Creator: AerialModel
Published: 2026-05-20T14:09:16
License: https://www.aerialmodel.com/Home/Policies

Interactive 3D model • Sainte Luce, Le Marin, Martinique • Processed May 20, 2026

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Site context

About this 3D model

This page displays an interactive drone 3D model of Charming T2 In A Green Setting Air Conditioned Bungalow With Stunning Sea View One Bedroom Apartment in Sainte Luce, Le Marin, Martinique. The viewer above is the primary experience: pan, zoom, switch visualization modes, and inspect the capture without installing extra software.

It can help review roofs and facades, grade and elevation changes, surface materials, vegetation, and overall site layout. The available project data includes the browser point cloud, textured OBJ model when available, orthophoto, and LAZ point-cloud archive, depending on the outputs generated for this scan.

Processed: May 20, 2026
Location: Sainte Luce, Le Marin, Martinique
Center coordinates: 14.47367° N, 60.94093° W
Capture type: Interactive 3D point-cloud model

Common questions

Yes. When downloads are available for this capture, use the Download menu for the textured OBJ model, orthophoto GeoTIFF, or point-cloud archive. Some files may require signing in.

It is useful for documentation, inspections, planning, progress tracking, asset review, and browser-based visual context for a site or property.

Overlapping aerial photos were processed into web-viewable photogrammetry data. Coordinates are shown from the project location and, where available, from the point-cloud projection.

Current point cloud URL

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✂️ Delete, crop, cut, and copy

Select unwanted points, remove noise, crop problem areas, copy useful regions, or paste patches into cleaner deliverables.

🧱 Work with layers

Keep original scans, pasted selections, cleanup regions, and repair patches organized as separate layers with visibility controls.

🔀 Merge and align scans

Move, rotate, scale, and align point-cloud layers so multiple captures can be combined into one shared workspace.

🎯 Select exactly what matters

Use lasso and box-selection workflows to isolate buildings, trees, ground areas, gaps, or cleanup regions.

📍 Annotate and measure

Add notes, distances, angles, areas, and height checks directly on the point cloud so review context stays attached to the data.

⬇️ Export cleaner results

Package the edited result for delivery after cleanup, alignment, visibility changes, and review decisions are complete.

Best fit: Use PointCloudEditor when this model needs cleanup before delivery, when multiple scans need to be aligned, when a client needs review notes, or when you want the final exported point cloud to match the edited browser view.

AerialTwin is designed for mapping, photogrammetry, construction progress, infrastructure documentation, and site monitoring workflows where a single drone scan is not enough. Instead of treating each model or orthomosaic as a separate deliverable, AerialTwin lets you organize scans into a timeline so clients, teams, and stakeholders can see how a property, project, or asset changes over time.

This is useful when you capture the same site repeatedly. Each imported point cloud, 3D model, map, or orthomosaic can become part of a dated site history. Users can move through the timeline, compare older and newer scans, review what changed, and keep visual records tied to the actual location.

Progress tracking

Show how a jobsite, property, asset, or landscape changed between drone flights instead of only delivering isolated files.

Scan-to-scan comparison

Help users compare drone scans over time, measure differences, and understand what physically changed on the site.

Context attached to the site

Add notes, photos, and videos to specific maps or point clouds so important field observations stay connected to the visual record.

Easy AerialModel imports

AerialTwin is intended to work naturally with AerialModel outputs, making it easier to bring processed drone scans into a timeline-based digital twin workflow.

Why this matters: For mapping and photogrammetry users, AerialTwin helps turn drone deliverables into an ongoing site intelligence product. Instead of only giving someone a model, map, or downloadable file, you can give them a place to revisit, compare, measure, annotate, and monitor over time.

AerialTwin allows drone scans to become the visual foundation for site monitoring. After a point cloud, map, orthomosaic, or 3D model is imported, sensors can be placed in the context of the site so users can understand where data is coming from and how conditions are changing between flights.

This is valuable because drone captures are periodic, while site conditions can change continuously. By attaching sensors to imported scans, AerialTwin can help bridge the gap between drone flights. A user can review the latest scan, see where sensors are located, monitor changing values, and configure alerts around conditions that matter for the site.

Monitor between flights

Use sensors to keep track of site conditions after the drone has left, giving teams a more continuous view of what is happening.

Custom alerts

Create alerts around important sensor values so users can be notified when conditions move outside expected ranges.

Spatial context

Place sensor information directly in relation to maps, models, and point clouds so the data is easier to understand visually.

AerialModel workflow support

Drone scans processed through AerialModel can be imported into AerialTwin so mapping outputs can become part of a monitored digital site record.

Why this matters: For photogrammetry users, sensors make drone scans more useful after delivery. The scan becomes a reference layer for live or recurring site information, helping clients and internal teams understand both what the site looked like during the flight and what is happening there afterward.

AerialTwin lets users add context directly onto drone scans instead of keeping important site information scattered across emails, folders, inspection notes, text messages, or separate photo libraries. A note can be tied to a specific location on a point cloud, 3D model, map, or orthomosaic, and it can also be tied to a specific point in the site's timeline.

This matters for monitoring because the drone scan shows what the site looked like at the time of capture, while attached notes, photos, and videos explain what was observed, why it mattered, and what follow-up may be needed. Instead of only viewing geometry or imagery, teams can build a living record of field observations directly on top of the site data.

Notes tied to location

Place a note on a specific part of the scan so users know exactly where an issue, observation, repair, hazard, or change was found.

Context tied to time

Connect notes to a date or timeline position so users can understand when something was observed and how it relates to older or newer drone captures.

Photos and videos

Attach close-up photos, inspection videos, walkthrough clips, or supporting field media to the same site location shown in the drone scan.

Monitoring history

Build a record of observations over time so clients and teams can track recurring issues, completed work, unresolved problems, and site changes between flights.

Better handoff

Give stakeholders more than a visual model. Notes explain what they should look at, what changed, and what action may be needed.

Easy AerialModel imports

Drone scans processed through AerialModel can be brought into AerialTwin, then enriched with notes, photos, videos, sensors, and timeline context.

Why this matters: For mapping and photogrammetry users, attached notes turn a scan into a site communication layer. The model or map shows the physical site, while notes, photos, and videos explain what is happening there, where it happened, when it happened, and why it matters for future monitoring.

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