#testcases — Public Fediverse posts

Urban Flood Observations [UFO] - A Hand-Labeled Training And Validation Dataset Of Post-Flood Inundation
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https://doi.org/10.48550/arXiv.2604.23066 <-- shared paper
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https://zenodo.org/records/19698577 <-- shared dataset
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H/T @Rohit Mukherjee
“UFO includes 215 high-resolution PlanetScope image chips and corresponding labels from 14 global flood events, with a focus on urban environments. The labels capture visible surface water in post-flood scenes.
Labeling floods from space is hard, especially in urban areas. Building shadows, narrow channels, wet soil, complex drainage features, and mixed pixels all make it difficult. [They] spent a lot of time refining the labels, and [they] think they can be useful for benchmarking flood-mapping methods and for training flood models (if you have PlanetScope access).
As an initial benchmark, [they have] trained a SegFormer model on the dataset and achieved a mean IoU of 77.3% under leave-one-event-out validation, where each flood event was held out entirely from training…”
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“Urban flooding affects lives and infrastructure worldwide. Mapping inundation in complex urban environments from satellite imagery remains challenging due to limited spatial resolution, infrequent acquisitions, and cloud cover. [They] present Urban Flood Observations (UFO), a global, hand-labeled dataset of post-flood inundation in diverse urban settings. UFO comprises 215 image chips (1024 by 1024 pixels) from 14 flood events between 2017 and 2021, derived from 3 metre PlanetScope imagery. Each chip is annotated with two classes: 'inundated' (all visible surface water, including floodwater and pre-existing water bodies (permanent or seasonal)) and 'non-inundated'. To demonstrate the dataset's utility, [they have] trained a segmentation model using leave-one-event-out cross-validation, achieving a mean Intersection over Union (IoU) of 77.3. [They] also used UFO to evaluate two widely used surface water products, the Sentinel-1-based NASA IMPACT model and Google's 10 m Dynamic World water class, which yielded IoUs of 44.1 and 48.1, respectively. UFO is publicly available to support the development and validation of urban inundation mapping methods…”
#UrbanFloodObservations #Urban #Flood #Observations #flooding #UFO #PlanetScope #remotesensing #GIS #spatial #mapping #opensource #opendata #floodmapping #model #modeling #floodmodels #infrastructure #water #hydrology #extremeweather #hydrography #humanimpacts #cost #economics #risk #hazard #segmentationmodel #elevation #topography #surfacewater #satellite #senteniel #IMPACT #testcases

I frequently see this pop up and I chuckle:

A #SQA person walks into a bar and orders:

* a pint of beer
* 2 pints of beer
* 0 pints of beer
* 999999999 pints of beer
* a lizard
* -1 pints of beer
* qwertyuip pints of beer

The first real customer walks into the bar and asks where the restrooom is. The bar bursts into flames and everyone dies.

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What other "walks into a bar" #software #testcases are we missing? #qa #development

#today is almost like the old days working in the #City (except for the pay!) - #coding / #debugging across multiple screens, writing #testCases, ...

(Also listening to #Garbage Version 2.0, loud...)

Well at least I peeled enough of the #onion that my #testcases found a new #bug. And with the fix for it in flight, I felt justified in starting to #document how we’re going to use this crazy thing that has sucked up so much of my time for the past two weeks.

Just like numerous development teams globally, our QA team is also delving into ChatGPT to enhance their work efficiency. Here's a brief insight from our QA team on their initial experience of utilizing ChatGPT for crafting test cases. https://blog.oursky.com/2023/04/14/ai-vs-human-test-case-generation/ 🚀🌐 #QA #ChatGPT #efficiency #testcases