Enhancing Land Cover Mapping by Reusing Historical Data through Feature Disentanglement and Contrastive Learning

מושגי ליבה

Combining historical and recent reference data through feature disentanglement and contrastive learning can enhance the accuracy of land cover mapping.

תקציר

The article presents a deep learning framework called REFeD that aims to enhance the accuracy of land cover mapping by effectively combining Earth observation (EO) data and reference data from two different domains, such as historical and recent data.
The key highlights are:

REFeD adopts a pseudo-siamese network architecture with unshared parameters to disentangle domain-invariant and domain-specific features. The domain-invariant features are then used for the final land cover classification.

The disentanglement process is achieved through contrastive learning, which structures the representation manifold to separate domain-invariant and domain-specific information.

REFeD also integrates an effective multi-level supervision scheme to further enforce the disentanglement of features.

Extensive experiments on two diverse study sites, Koumbia (Burkina Faso) and Centre Val de Loire (France), demonstrate that REFeD systematically outperforms baseline and state-of-the-art domain adaptation/generalization approaches. The results highlight the value of reusing historical reference data to enhance the accuracy of current land cover mapping.

Qualitative analysis of the land cover maps and the internal feature representations learned by REFeD provide additional insights into its superior performance compared to competing methods.

סטטיסטיקה

"Timely up-to-date land use/land cover (LULC) maps play a pivotal role in supporting agricultural territory management, environmental monitoring and facilitating well-informed and sustainable decision-making."
"How to make value of historical data, from the same or similar study sites, to enhance the current LULC mapping process constitutes a significant challenge that could enable the financial and human-resource efforts invested in previous data campaigns to be valued again."

ציטוטים

"Typically, for the creation of LC maps over a region at a certain period of time, ground truth data are collected at a particular moment through expensive and time-demanding field campaigns. These data are then utilized in conjunction with SITS information through advanced machine learning algorithms (Zhong et al., 2019) to get the final LC map."
"Once served its purpose, ground truth data will be disregarded loosing any further relevance. Furthermore, when the process is repeated (e.g., estimate agricultural production or potential biodiversity loss for a new year for the same or a related study site), new field campaigns must be afforded again with, in general, no way to profit from previous efforts."

תובנות מפתח מזוקקות מ:

Reuse out-of-year data to enhance land cover mappingvia feature disentanglement and contrastive learning

by Cassio F. Da... ב- arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11114.pdf

שאלות מעמיקות

How can the proposed framework be extended to handle multi-source remote sensing data, where the two domains are described by different sensor modalities

To extend the proposed framework to handle multi-source remote sensing data with different sensor modalities, several adjustments and considerations need to be made. Here are some key steps to achieve this:

Data Preprocessing:

Normalize the data from different sensors to ensure consistency in the feature space.
Perform feature alignment techniques to match the characteristics of the data from different sensors.

Model Architecture:

Modify the network architecture to accommodate multiple input modalities.
Incorporate sensor-specific branches in the model to extract features from each sensor independently.
Implement fusion strategies to combine features from different sensors effectively.

Loss Function:

Adapt the loss function to account for the multi-source nature of the data.
Introduce domain-specific loss terms to handle the discrepancies between sensor modalities.

Training Strategy:

Train the model on data from each sensor separately to learn sensor-specific features.
Fine-tune the model on a combined dataset to leverage the complementary information from different sensors.

By incorporating these adjustments, the framework can effectively handle multi-source remote sensing data with different sensor modalities, enabling more comprehensive and accurate analysis across diverse data sources.

What are the potential limitations of the feature disentanglement approach in scenarios where the domain shift between historical and recent data is too large

While feature disentanglement is a powerful technique for handling domain shifts in data, there are potential limitations in scenarios where the domain shift between historical and recent data is too large:

Loss of Information:

In cases of significant domain shifts, disentangling features may lead to the loss of crucial information that is essential for accurate classification.

Complexity of Disentanglement:

Large domain shifts can make it challenging to disentangle domain-specific and domain-invariant features effectively, leading to suboptimal performance.

Overfitting:

The model may struggle to generalize well to unseen data if the domain shift is too large, resulting in overfitting to the training data.

Limited Transferability:

Features learned from historical data with a substantial domain shift may not transfer well to recent data, reducing the effectiveness of the disentanglement approach.

In such scenarios, it is crucial to carefully assess the magnitude of the domain shift and potentially explore alternative techniques or data augmentation strategies to address the limitations of feature disentanglement.

Can the insights gained from this work on reusing historical data be applied to other geospatial data analysis tasks beyond land cover mapping, such as crop type classification or change detection

The insights gained from reusing historical data in land cover mapping can indeed be applied to other geospatial data analysis tasks beyond land cover mapping, such as crop type classification or change detection. Here's how these insights can be leveraged in different geospatial tasks:

Crop Type Classification:

Historical data can provide valuable information about crop patterns and seasonal variations, enhancing the classification of different crop types over time.
By reusing historical data, models can learn from past crop distributions and changes, improving the accuracy of crop type classification models.

Change Detection:

Historical data can serve as a baseline for detecting changes in land cover or crop types over time.
By comparing historical and recent data, models can identify areas of change, such as urban expansion, deforestation, or agricultural shifts.

Environmental Monitoring:

Insights from reusing historical data can aid in monitoring environmental changes, such as deforestation rates, water body fluctuations, or habitat loss.
By analyzing historical trends, models can predict future environmental changes and support conservation efforts.

By applying the principles of data reuse and leveraging historical data in various geospatial tasks, researchers and practitioners can improve the accuracy and efficiency of analyses across different domains within the field of remote sensing and geospatial analysis.