#Queensland and Northern NSW hit by catastrophic flood event. 8 lives lost and others still missing. 15,000 homes inundated and 54,000 without power in Brisbane alone. We are here to support communities as they recover and build #resilience

For the many Twitter folk asking if the cows and calves swimming in floodwaters in #tatham #NSWFloods made it to safety. They did. Here's the story with details. indynr.com/tatham-we-save…

Shout-out to our Fire and Rescue Service swiftwater rescuers ❤️ In the past fortnight these awesome firies responded to more than 650 water rescues. For these highly-skilled swiftwater technicians, that’s a lot of hours spent in dangerous conditions to help save others.

Do you use waze? Then you can help! Thanks to our partnership with RISE Resilience Innovations & FloodMapp, we're working to help you avoid flooded streets. Just verify if a street is flooded in the app. The more users validate, the better the modeling becomes! bit.ly/3HVELs4

Amazing that moment when you see a local startup get the Recognition they Deserve. Congratulations Juliette Murphy & Rayan and the rest of the awesome FloodMapp team

Exactly! Crowd sourcing a safer future!

Such a great write up Kate Saunders. I 100% agree with your conclusions, and that pictures are often worth 1000 words.

Watching @edm_barry talk to Queensland AI about some of the strategies we use for dealing with unreliable input data at FloodMapp

Scott Morrison is TERRIFIED of this happening over Easter: #auspol

We have some news.📢 With the support of founding partner Telstra Foundation, we've awarded $100K to three organisations harnessing the power of #techforgood. 💥Climasens 💥GoodHood 💥FloodMapp 1/2

Stay safe City of Norfolk, VA and download the waze app. Thanks to RISE Resilience Innovations, our real-time flood prediction technology will mark flood hazards and road closures in waze to help drivers navigate around flooded roads. We're here to help! #flooding #turnarounddontdrown

We are very excited to be attending #ASFPM2022 conference in Orlando FL! Please come and say hello to our team Mark Slauter and Juliette Murphy at booth 801! Association of State Floodplain Managers

Fascinating HN comment from someone who's company built a custom distributed data warehouse using compressed SQLite DB files in S3 that were queried using Lambda functions orchestrated by PostgreSQL running a custom foreign data wrapper news.ycombinator.com/item?id=314877…

Ever struggled using PyTorch to train deep learning on Geospatial data? Say no more. We developed a system (GeoTorch) that makes spatiotemporal deep learning in PyTorch a cakewalk👇🏽 kanchanchy.github.io/geotorch/ #geospatial #DeepLearning #ArtificialIntelligence #DataScience

An interesting case study on how FloodMapp #NowCast & #RoadSafe delivered live flood intelligence for real-time traffic routing. All thanks to our partners at RISE Resilience Innovations City of Norfolk, VA waze. Visit our website to learn more: floodmapp.com/nowcast-virgin…

FloodMapp carries out a cycle of #qualityassurance tasks for our flood models. This week our team has been working on QA activities at #Surat. These show our model extent comparisons for a major flood (height of 10.9m) on 6 Dec 21 & a minor flood (height of 6.6m) on 19 Sept 22.

The holiday season looks different for all our team - whether you’re Senior Flood Engineer Kelsey in Denver, Hydrology Lead Rob in Sydney or Graphic Designer Katie in Calgary. Wherever you are in the world, we hope you get some time to reflect, relax and recharge this week.

We'd love for you to come and join our Webinar on Flood intelligence and how it supports the tough decisions emergency managers have to make.

FloodMapp data is helping drivers avoid flooded roads and get home safely. Our thoughts are with Norfolk, Virginia as they recover from the flooding. Learn more about how FloodMapp can help your community here: floodmapp.com

Thrilled to unveil our latest work: multi-modal machine learning to forecast localized weather! We construct a graph neural network to learn dynamics at point locations, where typical gridded forecasts miss significant variation. Paper: arxiv.org/abs/2410.12938 🧵