Efficient Neural Embedding Compression for Earth Observation Modeling

Conceptos Básicos

Neural Embedding Compression (NEC) is a data-efficient approach for multi-task Earth Observation modeling, achieving significant data reduction with minimal performance loss.

Resumen

Abstract:

Introduces Neural Embedding Compression (NEC) for Earth Observation (EO) tasks.
Adapts foundation models (FM) through learned neural compression.
Evaluates NEC on scene classification and semantic segmentation tasks.

Introduction:

EO repositories face challenges due to large data transfers.
Machine learning methods, including deep learning, are adopted for processing EO data.

Method:

NEC optimizes a rate-distortion objective for multi-task embeddings.
Distortion term based on self-supervised learning, rate term computed using entropy models.

Experiments:

NEC training on the MillionAid dataset with reduced learning rate and batch size.
Benchmarking NEC against Raw Data Compression (RDC) and Uniformly Quantized Embeddings (UQE).

Results:

NEC shows superior performance in scene classification and semantic segmentation tasks.
Demonstrates practical viability with minimal performance gap between RDC and NEC.

Conclusion:

NEC offers a sustainable approach for EO tasks, reducing data transfer and storage requirements.

Estadísticas

NEC achieves similar accuracy with a 75% to 90% reduction in data.
Performance drops by only 5% on the scene classification task even at 99.7% compression.

Citas

"NEC is a data-efficient yet performant approach for multi-task EO modelling."
"NEC demonstrates the effectiveness of using compressed embeddings in downstream tasks."

Ideas clave extraídas de

Compressed Multi-task embeddings for Data-Efficient Downstream training and inference in Earth Observation

by Carlos Gomes... a las arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17886.pdf

Consultas más profundas

How can NEC impact the scalability of Earth Observation tasks in the future

Neural Embedding Compression (NEC) can significantly impact the scalability of Earth Observation tasks in the future by reducing data transfer and storage costs, thus making it more efficient and cost-effective to handle large volumes of EO data. By transmitting compressed embeddings instead of raw data, NEC minimizes the amount of data that needs to be transferred between data producers and consumers. This reduction in data size leads to lower latency, decreased energy consumption, and reduced storage requirements. As a result, NEC enables faster and more streamlined processing of EO data, making it easier to handle the vast amounts of information generated by Earth observation repositories. Additionally, the ability to adapt foundation models (FMs) through learned neural compression allows for multi-task embeddings to be generated efficiently, further enhancing the scalability of EO tasks by optimizing resource utilization and model performance.

What are the potential drawbacks or limitations of using Neural Embedding Compression

While Neural Embedding Compression (NEC) offers significant benefits for Earth Observation tasks, there are potential drawbacks and limitations to consider. One limitation is the trade-off between compression rate and embedding utility. As the compression rate increases, there may be a corresponding decrease in the quality of the embeddings, leading to a loss of information and potentially impacting the performance of downstream tasks. Additionally, the computational complexity of training and fine-tuning models with NEC can be a limitation, especially when dealing with large-scale EO datasets. The need for specialized expertise in neural compression techniques and model adaptation could also pose a challenge for widespread adoption. Furthermore, the distortion metric used in the compression process may not always capture the full complexity of the data, potentially leading to suboptimal compression results in certain scenarios.

How might Neural Embedding Compression be applied to other domains beyond Earth Observation

Neural Embedding Compression (NEC) has the potential to be applied to various domains beyond Earth Observation, offering data-efficient and performant solutions for a wide range of tasks. In the field of healthcare, NEC could be utilized for medical image analysis, where large volumes of imaging data need to be processed efficiently. By compressing embeddings and transferring them instead of raw data, healthcare systems can reduce data transfer costs and improve the scalability of medical imaging tasks. In the financial sector, NEC could enhance fraud detection systems by optimizing the processing of transaction data through compressed embeddings. Moreover, in the field of natural language processing, NEC could be applied to text data for tasks such as sentiment analysis and language translation, enabling more efficient processing and analysis of textual information. Overall, the principles of Neural Embedding Compression can be adapted and extended to various domains to enhance data efficiency and scalability in diverse applications.