Does this timeframe match the creation timeframe of the data associated with the instances (e.g., recent crawl of old news articles)? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . LCCS comprises of 23 classes and 14 sub-classes. The dataset, along with its machine-readable metadata, is hosted on CERN-backed Zenodo data repository: https://zenodo.org/record/6810792 Its long-term maintenance is discussed in the Datasheet. This includes reproducible code for the Benchmarks of Section 4 of [Cornebise et al., 2022a], following the ML Reproducibility Checklist [Pineau et al., 2021a,b]. The project also has its own website available at https://worldstrat.github.io/, The authors hereby state that they bear all responsibility in case of violation of rights, etc., and confirm that the data license is as follows: The low-resolution imagery, labels, metadata, and pretrained models are released under Creative Commons with Attribution 4.0 International (CC BY 4.0) The mean of the cloud coverage over the Sentinel 2 product areas is 7.98 %, with a standard deviation of 14.22. The quantiles are: 0.025: 0.00% 0.25: 0.00% 0.5: 0.66% 0.75: 10.05% 0.975: 49.95% It is important to note that this cloud cover percentage, as mentioned in the article and datasheet, is calculated on the entire product size of the provider, which varies in size but is much larger than the 2.5km we target. This means that even an image with a large cloud cover percentage can be cloud free, and in extreme cases (though unlikely), vice-versa. Also there are indeed considerable difference across sampled regions and land cover types. A simple example would be rainforests and non-desert equatorial regions. Using a strict no-cloud policy would make sampling enough low-resolution images either impossible or would make the temporal difference extremely large (up to 7 years for some AOIs). With that in mind, we strived to keep the cloud coverage as low as possible, ideally under 5%, while maintaining the temporal difference as small as possible.

The acorn is the fruit of the oak and is an important crop in the Spanish dehesa extreme\~na, especially for the value it provides in the Iberian pig food to obtain the "acorn" certification. For this reason, we want to maximise the production of Iberian pigs with the appropriate weight. Hence the need to know the area covered by the crowns of the acorn trees, to determine the covered wooded area (CWA, from the Spanish Superficie Arbolada Cubierta SAC) and thereby estimate the number of Iberian pigs that can be released per hectare, as indicated by the royal decree 4/2014. In this work, we propose the automatic estimation of the CWA, through aerial digital images (orthophotos) of the pastureland of Extremadura, and with this, to offer the possibility of determining the number of Iberian pigs to be released in a specific plot of land. Among the main issues for automatic detection are, first, the correct identification of acorn trees, secondly, correctly discriminating the shades of the acorn trees and, finally, detect the arbuscles (young acorn trees not yet productive, or shrubs that are not oaks). These difficulties represent a real challenge, both for the automatic segmentation process and for manual segmentation. In this work, the proposed method for automatic segmentation is based on the clustering algorithm proposed by Gustafson-Kessel (GK) but the modified version of Babuska (GK-B) and on the use of real orthophotos. The obtained results are promising both in their comparison with the real images and when compared with the images segmented by hand. The whole set of orthophotos used in this work correspond to an approximate area of 142 hectares, and the results are of great interest to producers of certified "acorn" pork.

Monitoring the distribution and size structure of long-living shrubs, such as Juniperus communis, can be used to estimate the long-term effects of climate change on high-mountain and high latitude ecosystems. Historical aerial very-high resolution imagery offers a retrospective tool to monitor shrub growth and distribution at high precision. Currently, deep learning models provide impressive results for detecting and delineating the contour of objects with defined shapes. However, adapting these models to detect natural objects that express complex growth patterns, such as junipers, is still a challenging task. This research presents a novel approach that leverages remotely sensed RGB imagery in conjunction with Mask R-CNN-based instance segmentation models to individually delineate Juniperus shrubs above the treeline in Sierra Nevada (Spain). In this study, we propose a new data construction design that consists in using photo interpreted (PI) and field work (FW) data to respectively develop and externally validate the model. We also propose a new shrub-tailored evaluation algorithm based on a new metric called Multiple Intersections over Ground Truth Area (MIoGTA) to assess and optimize the model shrub delineation performance. Finally, we deploy the developed model for the first time to generate a wall-to-wall map of Juniperus individuals. The experimental results demonstrate the efficiency of our dual data construction approach in overcoming the limitations associated with traditional field survey methods. They also highlight the robustness of MIoGTA metric in evaluating instance segmentation models on species with complex growth patterns showing more resilience against data annotation uncertainty. Furthermore, they show the effectiveness of employing Mask R-CNN with ResNet101-C4 backbone in delineating PI and FW shrubs, achieving an F1-score of 87,87% and 76.86%, respectively.

We propose a novel hierarchical agent architecture for multi-agent reinforcement learning with concealed information. The hierarchy is grounded in the concealed information about other players, which resolves "the chicken or the egg" nature of option discovery. We factorise the value function over a latent representation of the concealed information and then re-use this latent space to factorise the policy into options. Low-level policies (options) are trained to respond to particular states of other agents grouped by the latent representation, while the top level (meta-policy) learns to infer the latent representation from its own observation thereby to select the right option. This grounding facilitates credit assignment across the levels of hierarchy. We show that this helps generalisation---performance against a held-out set of pre-trained competitors, while training in self- or population-play---and resolution of social dilemmas in self-play.

Whether you're talking turbochargers or superchargers, forced induction is the name of the game these days. All the European brands use it. Cadillac has a couple, as do Lexus and Lincoln. Acura is still a holdout, but its twin-turbo NSX supercar indicates that may soon change. The siren song of more efficient power is simply too hard to resist.