Search results for "flood detection"

showing 2 items of 2 documents

Flood Detection On Low Cost Orbital Hardware

2019

Satellite imaging is a critical technology for monitoring and responding to natural disasters such as flooding. Despite the capabilities of modern satellites, there is still much to be desired from the perspective of first response organisations like UNICEF. Two main challenges are rapid access to data, and the ability to automatically identify flooded regions in images. We describe a prototypical flood segmentation system, identifying cloud, water and land, that could be deployed on a constellation of small satellites, performing processing on board to reduce downlink bandwidth by 2 orders of magnitude. We target PhiSat-1, part of the FSSCAT mission, which is planned to be launched by the …

FOS: Computer and information sciences: Computer science [C05] [Engineering computing & technology]Computer Science - Machine LearningImage and Video Processing (eess.IV): Multidisciplinary general & others [C99] [Engineering computing & technology]Machine Learning (stat.ML)Image and Video ProcessingElectrical Engineering and Systems Science - Image and Video Processing: Sciences informatiques [C05] [Ingénierie informatique & technologie]Machine Learning (cs.LG)Machine Learning: Multidisciplinaire généralités & autres [C99] [Ingénierie informatique & technologie]Artificial IntelligenceStatistics - Machine LearningSmall SatellitesFOS: Electrical engineering electronic engineering information engineeringFlood detectionEarth Observation: Aerospace & aeronautics engineering [C01] [Engineering computing & technology]: Ingénierie aérospatiale [C01] [Ingénierie informatique & technologie]
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Transfer Learning of Deep Learning Models for Cloud Masking in Optical Satellite Images

2023

Los satélites de observación de la Tierra proporcionan una oportunidad sin precedentes para monitorizar nuestro planeta a alta resolución tanto espacial como temporal. Sin embargo, para procesar toda esta cantidad creciente de datos, necesitamos desarrollar modelos rápidos y precisos adaptados a las características específicas de los datos de cada sensor. Para los sensores ópticos, detectar las nubes en la imagen es un primer paso inevitable en la mayoría de aplicaciones tanto terrestres como oceánicas. Aunque detectar nubes brillantes y opacas es relativamente fácil, identificar automáticamente nubes delgadas semitransparentes o diferenciar nubes de nieve o superficies brillantes es mucho …

machine learningflood detectioncloud maskingtransfer learningUNESCO::CIENCIAS TECNOLÓGICAS
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