7 research outputs found
Assessment of IBM and NASA's geospatial foundation model in flood inundation mapping
Vision foundation models are a new frontier in GeoAI research because of
their potential to enable powerful image analysis by learning and extracting
important image features from vast amounts of geospatial data. This paper
evaluates the performance of the first-of-its-kind geospatial foundation model,
IBM-NASA's Prithvi, to support a crucial geospatial analysis task: flood
inundation mapping. This model is compared with popular convolutional neural
network and vision transformer-based architectures in terms of mapping accuracy
for flooded areas. A benchmark dataset, Sen1Floods11, is used in the
experiments, and the models' predictability, generalizability, and
transferability are evaluated based on both a test dataset and a dataset that
is completely unseen by the model. Results show the impressive transferability
of the Prithvi model, highlighting its performance advantages in segmenting
flooded areas in previously unseen regions. The findings also suggest areas for
improvement for the Prithvi model in terms of adopting multi-scale
representation learning, developing more end-to-end pipelines for high-level
image analysis tasks, and offering more flexibility in terms of input data
bands.Comment: 11 pages, 4 figure
Automated location correction and spot height generation for named summits in the coterminous United States
Spot elevations published on historical U.S. Geological Survey topographic maps were established as needed to enhance information imparted by the quadrangle’s contours. In addition to other features, labels were routinely placed on mountain summits. While some elevations were established through field survey triangulation, many were computed during photogrammetric stereo-compilation. Today, Global Navigation Satellite System (GNSS) receivers have replaced expensive triangulation methods. However, since GNSS measurements require visiting the feature location, a national dataset containing high-accuracy spot elevations has not yet been created. Consequently, modern U.S. Topo maps are devoid of mountain peak or other spot elevations. Still, topographic map users continue to demand the display of spot heights. Therefore, a pilot study was conducted to evaluate the feasibility of automatically generating elevation values at named U.S. summits using available elevation data. The devised method uses an uphill stepping technique to find the most likely highest point in subsequently higher-resolution elevation models. Resulting elevation values are compared to other published sources. Results from 196 summits indicate that values derived from lidar are generally higher, whereas those populated from the one-third arc-second USGS Seamless 3DEP elevation dataset are generally lower. A thorough understanding of these relationships require the evaluation of more points