Boulders form from a variety of geological processes, which their size, shape, and orientation may help us better understand. Furthermore, they represent potential hazards to spacecraft landing that need to be characterized. However, mapping individual boulders across vast areas is extremely labor-intensive, often limiting the extent over which they are characterized and the statistical robustness of obtained boulder morphometrics. To automate boulder characterization, we use an instance segmentation neural network, Mask R-CNN, to detect and outline boulders in high-resolution satellite images. Our neural network, BoulderNet, was trained from a dataset of > 33,000 boulders in > 750 image tiles from Earth, the Moon, and Mars. BoulderNet not only correctly detects the majority of boulders in images, but it identifies the outline of boulders with high fidelity, achieving average precision and recall values of 72% and 64% relative to manually digitized boulders from the test dataset, when only detections with intersection-over-union ratios > 50% are considered valid. These values are similar to those obtained by human mappers. On Earth, equivalent boulder diameters, aspect ratios, and orientations extracted from predictions were benchmarked against ground measurements and yield values within 15%, 0.20, and 20 degrees of their ground-truth values, respectively. BoulderNet achieves better boulder detection and characterization performance relative to existing methods, providing a versatile open-source tool to characterize entire boulder fields on planetary surfaces.

One of California's riskiest volcanoes has for decades been undergoing geological changes and seismic activity, which are sometimes a precursor to an eruption, but -- thankfully -- no supervolcanic eruptions are expected. That's according to Caltech researchers who have been studying the Long Valley Caldera, which includes the Mammoth Lakes area in Mono County. The caldera was classified in 2018 by the U.S. Geological Survey as one of three volcanoes in the state -- along with 15 elsewhere in the U.S. -- considered a "very high threat," the highest-risk category defined by the agency. The two other volcanoes in California with that classification are Mt. Shasta in Siskiyou County and the Lassen Volcanic Center, which includes Lassen Peak in Shasta County.

Considered by some observers as more of an art than a science, avalanche forecasting involves a unique combination of human observation, analysis, and interpretation. Even the best forecasters only reach about 75% accuracy in their predictions. After all, they are human too and susceptible to bias and imperfection just like the rest of us. One of the biggest challenges in creating an accurate forecast lies in the fact that avalanche danger cannot be precisely measured and is therefore a matter of expert assessment (opinion). That was until last season when the Swiss Institute for Snow and Avalanche Research Group (SLF), a part of the National Swiss Federal Institute for Forest, Snow, and Landscape Research successfully tested a first-of-its-kind, artificial intelligence computer program to assist in its avalanche forecasts.