Greenland's ice is melting at an alarming rate, impacting sea levels worldwide🌍. Supraglacial lakes (SGLs) play a key role in this process, but accurately monitoring their depth and extent remains a challenge, highlighting the need for better methods to observe their extent and depth. Our deep learning approach is breaking new ground – as it has shown its feasibility for SGL monitoring, addressing data limitations and improving accuracy compared to traditional methods. 📚 Discover how we leverage deep learning to advance SGL monitoring techniques by reading this case study! https://lnkd.in/e3PzVvTK #Greenland #DeepLearning #Environment #Sustainability
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Thrilled to have presented my paper on "Air Suspended Particle Measurements in Downhill Areas Using Deep Learning Techniques" at ICCCCCM 2024. It was an incredible experience to share my research on leveraging deep learning for environmental monitoring and to engage with leading experts in the field. #ICCCCM2024 # #Deeplearning #Airquality #Reasearch #EnvironmentalScience
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Do We Really Need Deep Learning for Coastal Monitoring? An in-depth exploration of how machine learning stacks up against traditional coastal erosion monitoring methodsContinue reading on Towards Data Science »... https://lnkd.in/eZ2FFW2A #AI #ML #Automation
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Title->Solution Architect, Cert->TOGAF, Skills-> Cloud, Docker, Java, Kubernetes, Micro Services, SRE ..., Life->Spirit + Ritual + IST
Learning from Deep water dragonfish-STANDSTILL, OBSERVE, MANAGE Attributions @ https://lnkd.in/g2Za-Mk5
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💡 Learn about our recently released pre-trained deep learning models available in the ArcGIS Living Atlas of the World. #geoai #livingatlas #arcgis https://ow.ly/SlQL50QlrnC
ArcGIS AI Models – Year in Review
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Hacking Traffic Signals 🚦 with Data Science 👨💻 | Why did the traffic signal cross the road? Because the car was green!
Here is the final report for the Deep Learning Systems course I just finished. This will hopefully lead into a pilot project deploying 'Traffic Predictive' signal timing. Also, if you're interested in a Master's in #datascience, check out my article reviewing the courses I've taken. https://lnkd.in/g_VSzeRf
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📢 Minimizing the Effect of Specular Reflection on Object Detection and Pose Estimation of Bin Picking Systems Using Deep Learning by Daksith Jayasinghe, Chandima Abeysinghe, Ramitha Opanayaka, Randima Dinalankara, Bhagya Nathali Silva, Dr. (Eng.) Ruchire Eranga Wijesinghe and Udaya Wijenayake 📌 Full text: https://lnkd.in/g56ZiCvy #objectdetection #poseestimation #deeplearning
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Urban & Rural Planning Graduate | RS, GIS & Data Science Enthusiast | Machine Learning & Deep Learning Professional | ITS Enthusiast
🎓 Excited to share my latest research findings! 🎓 I have developed a deep learning-based traffic detection tool aimed at improving traffic estimation for Khulna City. This innovative approach leverages advanced machine learning techniques to enhance the accuracy and efficiency of traffic monitoring and management. 🔍 Key Highlights: Utilizes cutting-edge deep learning algorithms Provides real-time traffic estimation Enhances traffic flow and reduces congestion Check out the video below to see how this tool can revolutionize traffic management in urban areas. Your feedback and insights are highly appreciated! #DeepLearning #TrafficManagement #SmartCity #Research #KhulnaCity #UrbanPlanning #MachineLearning #Innovation
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Wonderful papr by Jan Pisl, where we map the tropics in terms of subsequent landuse after deforestation with satellite data! A great application of crowdsourcing, time series analysis and deep learning for Earth!
Why are we losing tropical forests? 🌴 Together with Devis Tuia, Marc Rußwurm, Jan Dirk Wegner, Lloyd Hughes, Gaston L. and Linda See, we used time series of satellite images to map the drivers of forest loss across tropics. To do this, we developed a deep learning model that combines convolutional, recurrent and attention modules. The model learns to focus on different time periods in each time series, identifying the distinct features of each driver. Read the full paper here: https://lnkd.in/esgJ8Apg Stay tuned as we are already working on a new model that is showing exciting improvements!
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#LatestPaper in MDPI Environments 📢🌍 Check out our latest article: An Evaluation of the Influence of Meteorological Factors and a #PollutantEmission Inventory on #PM2.5 Prediction in the Beijing–Tianjin–Hebei Region Based on a Deep Learning Method 🖊️by Xiaofei Shi, Bo Li, Xiaoxiao Gao, Stephen Dauda Yabo, Kun Wang, Hong Qi, Jie Ding, Donglei Fu and Wei Zhang 👉 Read the full article here: https://lnkd.in/dzZki8r3 #openaccess
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Thrilled to announce my debut blog post on leveraging deep learning for image classification with CNNs and transfer learning! In this journey through machine learning, I explore the transformative potential of CNNs and transfer learning using PyTorch. As my first foray into blogging, I'm eager to hear your thoughts and feedback. Let's learn and grow together! Check out the blog post here: https://lnkd.in/g_CSJ57H
Leveraging Deep Learning for Image Classification: A Journey Through CNNs and Transfer Learning
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