Ashish Patel 🇮🇳’s Post

𝗗𝗮𝘆-𝟮𝟵𝟳 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗜𝗻𝗳𝗲𝗿𝗿𝗶𝗻𝗴 𝗵𝗶𝗴𝗵-𝗿𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝘁𝗿𝗮𝗳𝗳𝗶𝗰 𝗮𝗰𝗰𝗶𝗱𝗲𝗻𝘁 𝗿𝗶𝘀𝗸 𝗺𝗮𝗽𝘀 𝗯𝗮𝘀𝗲𝗱 𝗼𝗻 𝘀𝗮𝘁𝗲𝗹𝗹𝗶𝘁𝗲 𝗶𝗺𝗮𝗴𝗲𝗿𝘆 𝗮𝗻𝗱 𝗚𝗣𝗦 𝘁𝗿𝗮𝗷𝗲𝗰𝘁𝗼𝗿𝗶𝗲𝘀 by Massachusetts Institute of Technology Follow me for a similar post: 🇮🇳 Ashish Patel Interesting Facts : 🔸 This paper is published in ICCV2021 with 1 Citation. ------------------------------------------------------------------- 𝗔𝗺𝗮𝘇𝗶𝗻𝗴 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵: https://lnkd.in/e3ffJXJ7 ------------------------------------------------------------------- 𝗜𝗠𝗣𝗢𝗥𝗧𝗔𝗡𝗖𝗘 🔸 Traffic accidents cost about 3% of the world’s GDP and are the leading cause of death in children and young adults. Accident risk maps are useful tools to monitor and mitigate accident risk. 🔸We present a technique to generate high- resolution (5 meters) accident risk maps. At this high resolution, accidents are sparse and risk estimation is limited by bias-variance trade-off. 🔸Prior accident risk maps either estimate low-resolution maps that are of low utility (high bias), or they use frequency-based estimation techniques that inaccurately predict where accidents actually happen (high variance). 🔸To improve this trade-off, we use an end- to-end deep architecture that can input satellite imagery, GPS trajectories, road maps and the history of accidents. Our evaluation on four metropolitan areas in the US with a total area of 7,488 km2 shows that our technique outper- form prior work in terms of resolution and accuracy. #computervision #artificialintelligence #deeplearning