New directions in mapping the Earth's surface with citizen science and generative AI.

As more satellite imagery has become openly available, efforts in mapping the Earth's surface have accelerated. Yet the accuracy of these maps is still limited by the lack of data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required.

Russian forest sequesters substantially more carbon than previously reported.

Since the collapse of the Soviet Union and transition to a new forest inventory system, Russia has reported almost no change in growing stock (+ 1.8%) and biomass (+ 0.6%).

The Forest Observation System, building a global reference dataset for remote sensing of forest biomass.

Forest biomass is an essential indicator for monitoring the Earth's ecosystems and climate. It is a critical input to greenhouse gas accounting, estimation of carbon losses and forest degradation, assessment of renewable energy potential, and for developing climate change mitigation policies such as REDD+, among others. Wall-to-wall mapping of aboveground biomass (AGB) is now possible with satellite remote sensing (RS).

Spatial distribution of arable and abandoned land across former Soviet Union countries.

Knowledge of the spatial distribution of agricultural abandonment following the collapse of the Soviet Union is highly uncertain. To help improve this situation, we have developed a new map of arable and abandoned land for 2010 at a 10 arc-second resolution. We have fused together existing land cover and land use maps at different temporal and spatial scales for the former Soviet Union (fSU) using a training data set collected from visual interpretation of very high resolution (VHR) imagery.