We propose an approach for early crop classification through identifying important timesteps with eXplainable AI (XAI) methods. Our approach consists of training a baseline crop classification model to carry out layer-wise relevance propagation (LRP) so that the salient time step can be identified. We chose a selected number of such important time indices to create the bounding region of the shortest possible classification timeframe. We identified the period 21st April 2019 to 9th August 2019 as having the best trade-off in terms of accuracy and earliness. This timeframe only suffers a 0.75 % loss in accuracy as compared to using the full timeseries. We observed that the LRP-derived important timesteps also highlight small details in input values that differentiates between different classes and possibly offers links to physical crop growth milestones.

Machine learning (ML) can help fight pandemics like COVID-19 by enabling rapid screening of large volumes of images.To perform data analysis while maintaining patient privacy, we create ML models that satisfy Differential Privacy (DP).Previous works exploring private COVID-19 models are in part based on small datasets, provide weaker or unclear privacy guarantees, and do not investigate practical privacy.We suggest improvements to address these open gaps.We account for inherent class imbalances and evaluate the utility-privacy trade-off more extensively and over stricter privacy budgets.Our evaluation is supported by empirically estimating practical privacy through black-box Membership Inference Attacks (MIAs).The introduced DP should help limit leakage threats posed by MIAs, and our practical analysis is the first to test this hypothesis on the COVID-19 classification task.Our results indicate that needed privacy levels might differ based on the task-dependent practical threat from MIAs.The results further suggest that with increasing DP guarantees, empirical privacy leakage only improves marginally, and DP therefore appears to have a limited impact on practical MIA defense.Our findings identify possibilities for better utility-privacy trade-offs, and we believe that empirical attack-specific privacy estimation can play a vital role in tuning for practical privacy.

With leaps in machine learning techniques and their application on Earth observation challenges has unlocked unprecedented performance across the domain. While the further development of these methods was previously limited by the avail-ability and volume of sensor data and computing resources, the lack of adequate reference data is now constituting new bottlenecks. Since creating such ground-truth information is an expensive and error-prone task, new ways must be devised to source reliable, high-quality reference data on large scales. As an example, we showcase Eurocrops, a reference dataset for crop type classification that aggregates and harmonizes administrative data surveyed in different countries with the goal of transnational interoperability.