Depending on the number of acres in your Map Plan, DroneDeploy offers a recommended altitude. Having a lower flight altitude will take longer to fly, but it will increase your map resolution. Here is an explanation of the different preferences:įlight Altitude – Flight Altitude is how high your drone will fly AGL and determines the final resolution of your orthomosaic map. DroneDeploy Map Plan Flight SettingsĭroneDeploy allows a variety of preferences to customize your map detail, resolution, and images that will be captured. You want there to be a safe buffer between the edge of the flight plan and your property.įinally, you want to customize your flight preferences to make sure you capture the best data for your project. DroneDeploy recommends “overflying,” or creating your flight plan so it extends beyond the edges of your property. You can click on the grayed out circles at the center points of line segments to add additional points and customize your flight plan to match the shape of your property. Click and drag on the circles at the corners of your extend your flight path to cover your entire property. įrom the left-hand menu, select ‘New Map Plan’.ĭroneDeploy will place a new square box on the screen which will be the start to your new automated flight path. Then click ‘ Create project here ’.Įnter a name for your project and click ‘Continue’. Drag and position the box so it is directly above your property. DroneDeploy will place a box on the screen in the general location of the address you entered. To create a flight plan, log into your DroneDeploy account and select ‘ New Project ’ in the top left hand corner of the screen.Įnter the address of the property you’d like to fly. You can adjust the resolution of your map and even enabled enhanced 3D for more accurate and detailed 3D models. You have different options when creating your flight plan too. Step 1 – Create A Flight PathĭroneDeploy’s software allows you to create an automated flight plan that your drone will fly on to capture aerial photos ideal for creating orthomosaic maps and 3D models. We are working to support more collaborations and benchmarks in the future, especially on projects relevant to climate change and social good.Like this video? Consider subscribing to our YouTube channel for more like it. Benchmarks give us the opportunity to share datasets and host public collaboration, fostering a transparent environment around a meaningful application. Disaster relief: guiding first responders, especially in physically inaccessible areasĪt Weights & Biases, we are building tools to support ethical and effective development of machine learning models.Construction: monitoring safety and progress.Nature conservation: tracking wildlife populations and ecosystem change.Long-term impactįaster and more accurate models for aerial segmentation can help at a massive scale in many domains, including: From the benchmark, you can follow two examples (using FastAI or Keras) to reproduce initial baselines on the data and explore many potential improvements: data augmentation and post processing, hyperparameter and architecture tuning, integrating the elevation signal (not currently used), and more. Given a high-resolution photograph and elevation of an area mapped by a drone, how well can we locate the different types of objects or landscapes in the scene? The training data currently has six classes labeled (ground, water, vegetation, cars, clutter, and buildings) and an impressive level of detail at 10cm per pixel. The goal of this project is to collaborate on and advance scene understanding from drone data. Combine visual & elevation images to identify buildings, ground, vegetation, water, etc.
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