Note: you are viewing archived information about the workshop held in 2023. Information about the latest workshop can be found
here .
Large scale zonal summaries with Rust Michael Salib (Indigo Ag Inc.)
Show times in: BST [UTC+1] Pacific Time [UTC-7] Mountain Time [UTC-6] Central Time [UTC-5] Eastern Time [UTC-4] GMT [UTC±0] WEST [UTC+1] CEST [UTC+2] EEST [UTC+3] UTC-12 UTC-11 UTC-10 (Honolulu) UTC-9:30 UTC-9 UTC-8 (Anchorage) UTC-7 (Los Angeles, San Diego, Vancouver, Tijuana) UTC-6 (Denver, Edmonton, Cuidad Juárez, Guatemala City) UTC-5 (Houston, Winnipeg, Lima, Kingston) UTC-4 (Havana, New York, Washington D.C., Toronto, London, Caracas) UTC-3:30 UTC-3 (Santiago, Sao Pãulo, Rio de Janeiro, Montevideo, Buenos Aires) UTC-2 UTC-1 UTC (Dakar) UTC+1 (London, Lisbon, Lagos, Algiers, Dublin, King's Lynn) UTC+2 (Madrid, Vatican, Paris, Rome, Vienna, Warsaw, Germany, Cairo, Johannesburg) UTC+3 (Kyiv, Athens, Sofia, Addis Ababa, Istanbul, Moscow, Riyadh) UTC+3:30 (Tehran) UTC+4 (Dubai, Baku) UTC+4:30 (Kabul) UTC+5 (Karachi) UTC:5:30 (Mumbai, Columbo) UTC+5:45 (Kathmandu)) UTC+6 (Almaty, Dhaka) UTC+6:30 (Yangon) UTC+7 (Bangkok, Jakarta) UTC+8 (Perth, Bander Seri Begawan, Beijing, Singapore, Kuala Lumpur, Taipei, Shanghai) UTC+8:45 UTC+9 (Soeul, Tokyo, Pyongyang) UTC+9:30 (Adelaide) UTC+10 (Sydney, Vladivostok, Port Moresby) UTC+10:30 UTC+11 UTC+12 (Suva) UTC+12:45 UTC+13 (Wellington, Auckland) UTC+14 Unix time
I've designed a system that uses Rust to perform very large scale zonal summary operations of remote sensing data (satellite imagery, weather and soil data sets). We use AWS Batch and Lambda to extract subsets of pixels from our data sources and then summarize them into tabular parquet files. That enables machine learning pipelines and all manner of easy data analysis.