<p>The ESA-funded AIREO project [1] sets out to produce AI-ready training dataset specifications and best practices to support the training and development of machine learning models on Earth Observation (EO) data. While the quality and quantity of EO data has increased drastically over the past decades, availability of training data for machine learning applications is considered a major bottleneck. The goal is to move towards implementing FAIR data principles for training data in EO, enhancing especially the finability, interoperability and reusability aspects.&#160; To achieve this goal, AIREO sets out to provide a training data specification and to develop best practices for the use of training datasets in EO. An additional goal is to make training data sets self-explanatory (&#8220;AI-ready) in order to expose challenging problems to a wider audience that does not have expert geospatial knowledge.&#160;</p><p>Key elements that are addressed in the AIREO specification are granular and interoperable metadata (based on STAC), innovative Quality Assurance metrics, data provenance and processing history as well as integrated feature engineering recipes that optimize platform independence. Several initial pilot datasets are being developed following the AIREO data specifications. These pilot applications include for example&#160; forest biomass, sea ice detection and the estimation of atmospheric parameters.An API for the easy exploitation of these datasets will be provided.to allow the Training Datasets (TDS) to work against EO catalogs (based on OGC STAC catalogs and best practises from ML community) to allow updating and updated model training over time.</p><p>&#160;</p><p>This presentation will present the first version of the AIREO training dataset specification and will showcase some elements of the best-practices that were developed. The AIREO compliant pilot datasets will be presented which are openly accessible and community feedback is explicitly encouraged.&#160;</p><p><br><br>[1] https://aireo.net/</p>
Neural network pruning has been deemed essential in the deployment of deep neural networks on resourceconstrained edge devices, greatly reducing the number of network parameters without drastically compromising accuracy. A class of techniques proposed in the literature assigns an importance score to each parameter and prunes those of the least importance. However, most of these methods are based on generalised estimations of the importance of each parameter, ignoring the context of the specific task at hand. In this paper, we propose a task specific pruning approach, CSPrune, which is based on how efficiently a neuron or a convolutional filter is able to separate classes. Our axiomatic approach assigns an importance score based on how separable different classes are in the output activations or feature maps, preserving separation of classes which avoids reduction in classification accuracy. Additionally, most pruning algorithms prune individual connections or weights leading to a sparse network without taking into account whether the hardware the network is deployed on can take advantage of that sparsity or not. CSPrune prunes whole neurons or filters which results in a more structured pruned network whose sparsity can be more efficiently utilised by the hardware. We evaluate our pruning method against various benchmark datasets, both small and large, and network architectures and show that our approach outperforms comparable pruning techniques.Impact Statement-Deep Neural Networks (DNNs) have achieved state-of-the-art accuracy across a variety of tasks such as classification, semantic segmentation, robot navigation, etc. Typically, these DNNs contain a few hundred thousand to a few million parameters which require a lot of memory, computational resources, and power, which typical devices at the edge are severely constrained in. Several techniques like quantization, tensor decomposition, and pruning have been proposed to compress DNNs for easy deployment on these edge devices. Our novel pruning methodology, CSPrune, proposed in this work compresses the DNNs whilst preserving accuracy. We compare it with several other studies on large datasets and DNNs and show it performs better in many cases.
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