111,289 research outputs found
Evaluation Challenges for Geospatial ML
As geospatial machine learning models and maps derived from their predictions
are increasingly used for downstream analyses in science and policy, it is
imperative to evaluate their accuracy and applicability. Geospatial machine
learning has key distinctions from other learning paradigms, and as such, the
correct way to measure performance of spatial machine learning outputs has been
a topic of debate. In this paper, I delineate unique challenges of model
evaluation for geospatial machine learning with global or remotely sensed
datasets, culminating in concrete takeaways to improve evaluations of
geospatial model performance.Comment: ICLR 2023 Workshop on Machine Learning for Remote Sensin
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
Hyperspectral Unmixing with Endmember Variability using Partial Membership Latent Dirichlet Allocation
The application of Partial Membership Latent Dirichlet Allocation(PM-LDA) for
hyperspectral endmember estimation and spectral unmixing is presented. PM-LDA
provides a model for a hyperspectral image analysis that accounts for spectral
variability and incorporates spatial information through the use of
superpixel-based 'documents.' In our application of PM-LDA, we employ the
Normal Compositional Model in which endmembers are represented as Normal
distributions to account for spectral variability and proportion vectors are
modeled as random variables governed by a Dirichlet distribution. The use of
the Dirichlet distribution enforces positivity and sum-to-one constraints on
the proportion values. Algorithm results on real hyperspectral data indicate
that PM-LDA produces endmember distributions that represent the ground truth
classes and their associated variability
Recommended from our members
Supporting location-based inquiry learning across school, field and home contexts
Here we explore how technology can be applied to support inquiry learning spanning a range of contexts. The development process of a location-based inquiry learning toolset is presented for a secondary school GCSE Geography project. The design framework used and the process of participatory development is discussed with regard to the co-development of the activities and tools involved in an inquiry project. The lessons learned relate to the formation of a motivational context for the inquiry; the role of personal data collection in the field; the use of bridging representations across field and classroom activities; and the development of flexible, re-usable tools to support and bridge sequences of activities
- …