Bona Investment & Management Co.’s Post

This research paper presents a deep learning-based framework for road marking extraction, classification and modeling from three-dimensional #3D mobile laser scanning (MLS) point clouds. https://lnkd.in/gnAVVzmz

🛰️🌍 Object Detection from Satellite Images using Computer Vision & Roboflow! 🌍🛰️ I’m excited to share my latest project: leveraging computer vision and Roboflow to perform object detection on satellite images! 🚀🔍 Here’s the process: Dataset Preparation: Used Roboflow to curate and annotate a comprehensive dataset of satellite images. Their tools make it seamless to handle large-scale image datasets! 📸🖼️ Model Training: Trained a powerful YOLOv8 model on the dataset, optimizing for high accuracy in detecting objects like buildings, vehicles, and natural features. 🏠🚗🌳 Analysis and Results: Deployed the trained model to analyze new satellite images in real-time, achieving remarkable precision in object detection. The insights gathered are invaluable for urban planning, environmental monitoring, and disaster response. 📊🔍 Why this matters? 🤔 Enhanced Monitoring: Enables efficient monitoring of vast geographical areas. Timely Interventions: Helps in quick response to natural disasters and other critical events. Data-Driven Decisions: Facilitates better planning and resource allocation. Check out some sample detections and the impressive accuracy of the model. This is just the beginning – stay tuned for a detailed tutorial on how you can build and deploy your own satellite image detection system! 🌟🛰️ #Pyresearch #SatelliteImaging #ObjectDetection #ComputerVision #Roboflow #MachineLearning #AI #DeepLearning #TechInnovation #GeospatialAnalysis #SmartTech https://lnkd.in/ezDwP7He

💡 Question of the week: What post-processing software do you offer? ✨ There's a long(ish) answer here but it's important to highlight that the moments I most often hear: *sigh* "That's a relief" 😌 is when I tell customers that they are free to work with their desired platform/software - whether in house or external. We don't lock you in or hold your data hostage. ❌ 🔒 Today I’d like to highlight just one software platform and the potential it brings for our customers working with street view data captured using #Mosaic360Cameras. 🎥 This video by Simerse showcases AI automatically detecting infrastructure in the video: - Utility poles - Road signs - Other street-level infrastructure ⚙️ Simerse AI detects and flags the relevant infrastructure for the customer automatically. 👀 When first evaluating Mosaic camera system data in their platform, Michael Naber from Simerse said: "Certainly, the resolution is excellent. A big factor for us is how easy the data is to work with." You can learn more about our camera systems next week in person if you are at #intergeo2024 in Stuttgart! Stop by Booth D1.006 in Hall 1 See you there! Balazs Honti Andreas Rabenseifner #infrastructuremaintenance #geospatial #gis #assetdetection #ai #roadassets #utilityinspection #utilitysurveying

❗ Automated asset identification and feature extraction from high resolution panoramas! 📸 🚗 Image recognition models are definitely advanced by now, so it's only make sense to use them for city mapping. So we did, together with Simerse🏬 👷♀️ Classical geodesy could be still important for validation, or in areas requiring high - and by that I mean mm - accuracy, but for urban mapping, and creating "digital asset cadaster" I can't think of a simpler, faster and more cost-effective way than using Mosaic - Geospatial Imaging Leaders👨🔧

TBC 2024.10 Automatic Lane Line Markings Extraction. This video highlights the enhanced “Automatic Lane Line Marking Feature Extraction” command in TBC. This option enables you to automatically extract linestrings from pavement lane lines in images captured with a Trimble Mobile Mapping system that includes a 360-degree camera and laser scanner. Working with a pre-classified Ground point cloud region, a deep learning model is used to detect lines on each image frame and automatically map them to the point cloud to create 3D linestrings. Need demo datasets? Visit https://lnkd.in/gDkVTaf2 For questions, contact your local Trimble representatives and distributors. Find them here: https://lnkd.in/e5-_8hmG Geospatial: https://lnkd.in/e2J6fjKF Construction: https://lnkd.in/e3A_ZXri #tbc #trimble #trimblefieldsystems

🛰️ Revolutionizing Satellite Image Analysis with Computer Vision! 🌍 We’re leveraging the power of Object Detection from Satellite Images using advanced computer vision techniques with Roboflow! 🚀 This cutting-edge technology is enabling faster, more accurate detection of critical objects like infrastructure, land changes, and environmental patterns from space. 🔍 🌟 Key Highlights: Real-time satellite image processing 🕒 Custom object detection models with Roboflow 🔧 Applications in agriculture, urban planning, and disaster management 🌱🏙️ Harnessing satellite data has never been more impactful! Stay tuned as we unlock new insights from above. 🚁 #Pyresearch #AI #ComputerVision #SatelliteImagery #ObjectDetection #Roboflow #SpaceTech #UrbanPlanning #Agriculture #Innovation #DeepLearning #GeospatialAI https://lnkd.in/dDTRzDic

15 cm HD satellite imagery provides a more detailed, consistent, and reliable base for machine learning and artificial intelligence applications, making it possible to analyze and extract features with a level of precision previously unimaginable at scale.   I had the privilege of being interviewed by Geoawesome and sharing how Maxar Technologies’s Vivid Advanced offers a seamless, cloudless basemap at 15 cm HD resolution that ensures both machines and humans can rely on consistently high-quality imagery, no matter where they are in the world.   Have a read here: https://lnkd.in/gnGd8YSg   #satelliteimagery #mapping #navigation #digitalmaps

As geospatial professionals, we do everything we can to minimize both spatial and nonspatial error so that we deliver high quality products that deliver value to our customers. However, when it comes to #GeoAI, we do not have a set of best practices and standards like what American Society for Photogrammetry and Remote Sensing (ASPRS) publishes. Have a look at what I am talking about here: https://lnkd.in/eKid-XNp Our industry needs guidance based on sound science. I loathe the word interdisciplinary, but the measurement pros need to talk to the machine learning folks and try to agree on something and get that pushed out to the public. Should we assume our models have the same spatial accuracy as that of the coarsest dataset fed to our models? Does the ordering of remotely sensed imagery in a tensor affect spatial accuracy? How should models that make spatial predictions be evaluated and compared? I wish I had the answers, but I simply do not know... and am crossing my fingers that smarter folks have a look at this sooner rather than later.

Great blog post from the team on how our new digital twins, PD Replica, significantly reduce the domain gap between real and sim. Replica is better for testing and training your systems than classic simulation!

The distribution of LiDAR points over vegetation is not like that of other objects, as there will be points inside the vegetation also. Well, Aditya Kumar from my lab has exploited this fact and designed a novel DL architecture #GreenSegNet which is capable of vegetation segmentation. #GreenSegNet has shown the best performance among available SoTA architectures and is also among the fastest. Good job Aditya Kumar. We have tested the network for Mobile LiDAR data. Now we have to assess its performance for Aerial LiDAR also. In addition, we have to test it for transfer learning. Thanks to Jagannath Aryal, Stephan Winter, University of Melbourne Indian Institute of Technology, Kanpur Department of Science and Technology for supporting this work. #deeplearning #vegetationsegmentation #LiDAR