Search Results for author:
Found 6 papers, 1 papers with code
FREE: The Foundational Semantic Recognition for Modeling Environmental Ecosystems
This raises a fundamental question in advancing the modeling of environmental ecosystems: how to build a general framework for modeling the complex relationships amongst various environmental data over space and time?
Mapping smallholder cashew plantations to inform sustainable tree crop expansion in Benin
In particular, we developed a SpatioTemporal Classification with Attention (STCA) model to map the distribution of cashew plantations, which can fully capture texture information from discriminative time steps during a growing season.
Task-Adaptive Meta-Learning Framework for Advancing Spatial Generalizability
One major advantage of our proposed method is that it improves the model adaptation to a large number of heterogeneous tasks.
Early- and in-season crop type mapping without current-year ground truth: generating labels from historical information via a topology-based approach
Land cover classification in remote sensing is often faced with the challenge of limited ground truth.
Clustering augmented Self-Supervised Learning: Anapplication to Land Cover Mapping
Common techniques of addressing this issue, based on the underlying idea of pre-training the Deep Neural Networks (DNN) on freely available large datasets, cannot be used for Remote Sensing due to the unavailability of such large-scale labeled datasets and the heterogeneity of data sources caused by the varying spatial and spectral resolution of different sensors.
Attention-augmented Spatio-Temporal Segmentation for Land Cover Mapping
The availability of massive earth observing satellite data provide huge opportunities for land use and land cover mapping.
