ServiceNow Research

Remote Sensing

A General-Purpose Neural Architecture for Geospatial Systems
Geospatial Information Systems are used by researchers and Humanitarian Assistance and Disaster Response (HADR) practitioners to …
RaVAEn: Unsupervised Change Detection of Extreme Events Using ML On-Board Satellites
Applications such as disaster management enormously benefit from rapid availability of satellite observations. Traditionally, data …
Toward Foundation Models for Earth Monitoring: Proposal for a Climate Change Benchmark
Recent progress in self-supervision shows that pre-training large neural networks on vast amounts of unsupervised data can lead to …
Seasonal Contrast: Unsupervised Pre-Training from Uncurated Remote Sensing Data
Remote sensing and automatic earth monitoring are key to solve global-scale challenges such as disaster prevention, land use …
Counting Cows: Tracking Illegal Cattle Ranching From High-Resolution Satellite Imagery
Cattle farming is responsible for 8.8% of greenhouse gas emissions worldwide. In addition to the methane emitted due to their digestive …
Extending the Spatial Scale of Land Use Regression Models for Ambient Ultrafine Particles using Satellite Images and Deep Convolutional Neural Networks
We paired existing land use regression (LUR) models for ambient ultrafine particles in Montreal and Toronto, Canada with satellite …
Learning Global Variations in Outdoor PM_2.5 Concentrations with Satellite Images
Here we present a new method of estimating global variations in outdoor PM2.5 concentrations using satellite images combined with …