dataesr/scientific-paragraphs-categorization huggingface dataset:
Importantly, the use of datasets with multiple tasks allows for an in-depth benchmarking of the connectivity measures. It also highlights some advantages of machine learning over statistical testing in making use of the multivariate nature of connectivity measures, transposing and abstracting concepts presented in a recent study for subject identification  (Pallarés et al. , 2018). The fourth section bridges with network analysis  (Gilson et al. , 2019; Gilson, Kouvaris, Deco, & Zamora-López, 2018) , interpreting changes in MOU-EC connections in a collective and model-based manner. In particular, it adapts community detection to our dynamic model (Figure  2D ).
To illustrate our framework, we use data that were previously analyzed  (Demirtaş et al. , 2019; Gilson, Deco, et al. , 2018; Hlinka et al. , 2011; Mantini et al. , 2012) , whose details are summarized in Appendix A in the Supporting Information. Subjects in the scanner were recorded fixating on a point in a black screen (two rest sessions R1 and R2) or a movie. The entire movie session was divided in three sessions (M1, M2, and M3), each corresponding to a different part of the movie. The vectorized MOU-EC estimates are displayed for all subjects and sessions in Figure  3A. The question is then whether connectivity measures can capture information to identify the movie sessions individually. For example, the MOU-EC links indicated by the gray arrow exhibit strong changes between rest and movie (with small and large values, respectively).
Thus, the accuracy distribution provides an estimate of the generalization capability of the classification scheme to new samples. Different splitting strategies have been proposed, and here we use the recommended one for neuroimaging applications based on a ratio of 80%:20% for train and test sets, respectively  (Varoquaux et al. , 2017). We repeat the classification for 40 random splitting of the data. This is lower than the recommended 100 times and is justified by the rather low number of 22 subjects in our dataset, as explained in the box below together with further technical considerations about choosing a cross-validation scheme.
This would likely inflate the evaluated performance for generalization. A better solution is to apply the cross-validation to "groups" (subjects in this case) instead of single samples (fMRI sessions). While it is possible to repeat random resampling of the groups several times, the chance of repeating the cross-validation for the exact same test set becomes nonnegligible when the number of subjects is not sufficiently large. For our dataset, using four subjects for testing and the remaining 18 for training, there is almost 50% chance of getting the same split twice over 100 repetitions. The consequence would be an underestimation of performance variability. In such cases, an alternative valid option is the leave-one-group-out strategy, using a subject for testing and the remaining subjects for training, yielding a distribution of 22 accuracies.
This procedure also has the advantage of highlighting individual differences in the dataset, showing for example whether some subjects are easier or more difficult to predict. With our data, we find that repeating 80%:20% shuffle splits 40 times and performing leave-one-out for each of the 22 subjects leads to very similar accuracy distributions. We consider task discrimination in two flavors: movie versus rest (two tasks) in Figure  4B  and the movie sessions individually plus rest (4 tasks) in Figure  4C. For each case, the performance evaluated using the test set should be considered relative to perfect accuracy and chance level (dashed line). As expected from Figure  3 , the four-task discrimination is more difficult that the two-task one. The relative performance decreases by about a half of perfect accuracy for the four tasks compared with the two tasks.
There are three interesting points coming from the comparison of the performances between the connectivity measures. First, the MLR and LDA perform much better than the 1NN for the four-task discrimination, with both MOU-EC and FC; this does not change when using kNN instead of 1NN. This agrees with previous results on subject identification  (Pallarés et al. , 2018)  that are transposed to the present dataset in Figure  4E. For task identification, the Pearson-correlation similarity for FC or MOU-EC does not seem the best choice, especially when the environment is complex. A technical point here is that LDA takes a long time to be trained for a large number of features or for many labels (subjects in Figure  4E ). Over all these results, the MLR seems the best practical choice for a classifier, as was found for clinical data  (Dadi et al.
, 2019). It is worth stressing that the dynamic flow and communicability are graph-like measures that genuinely capture the properties of weighted networks, similar to the previously proposed graph communicability  (Estrada, 2013; Estrada & Hatano, 2008). Considering again the fMRI dataset in Appendix A in the Supporting Information, we examine the dynamic flow at various scales in the brain network for the two conditions, movie and rest. This complements our previous analysis of the same dataset that showed that the The propagation of extra activity resulting from a perturbation (here applied to ROI i in purple) develops over the integration time and then fades away. The dashed purple curve represents the corresponding activity increase for ROI k (for legibility purpose, the equivalent for ROI j is not displayed).
The alignment of distinct datasets with various MRI protocols also remains a difficult question for practical application to large datasets  (Varoquaux et al. , 2018). In parallel to the study of cognition, the presented framework can be applied to neuropathologies with the aim to inform clinical diagnosis  (Matthews & Hampshire, 2016). The rationale is that BOLD activity specifically reflects neuropathologies, even in the resting state  (Greicius, 2008; Hohenfeld et al. , 2018). If SC is increasingly used for strokes or Alzheimer's disease that strongly impact the brain anatomy, fMRI may provide additional information  (Habib et al. , 2017; Siegel et al. , 2016). Other diseases like depression  (Drysdale et al. , 2017)  or some motor disorders  (Rowe & Siebner, 2012 ) may be better investigated using functional imaging.
Moreover, without a proper experimental interaction with the brain activity like using stimulation, the notion of causality is subject to the initial model assumption  (Hughes, 1997; Kaplan, 2011). This remark also applies to other methods for directed connectivity, such as Granger causality and DCM  (Valdes-Sosa et al. , 2011). A key distinction of our approach compared with DCM is that our MOU-EC (or FC) estimates are calculated for each fMRI session, so we obtain a sample distribution for connection weight over all subjects for each condition (e. g. , task). In contrast, DCM usually considers a single model per condition that aims to represent the variability over subjects with a parametric distribution (mean and variance) for each connection weight. The limitations and advantages of both approaches in terms of statistical power and generalizability remain to be practically compared, especially for large datasets.
The characteristic backscatter response of closed-canopy oil palm stands in Sentinel-1 and the ability of CNN to learn spatial patterns, such as the harvest road networks, allowed the distinction between industrial and smallholder plantations globally (overall accuracy = 98. 52 ± 0. 20 %), outperforming the accuracy of existing regional oil palm datasets that used conventional machine-learning algorithms. The user's accuracy, reflecting commission error, in industrial and smallholders was 88. 22 ± 2. 73 % and 76. 56 ± 4. 53 %, and the producer's accuracy, reflecting omission error, was 75. 78 ± 3. 55 % and 86. 92 ± 5. 12 %, respectively. The global oil palm layer reveals that closed-canopy oil palm plantations are found in 49 countries, covering a mapped area of 19. 60 Mha; the area estimate was 21.
Overview The classification model for oil palm plantations used the Sentinel-1 and Sentinel-2 half-yearly composites as input images (Fig. 1 ). The maps presented in this study correspond to the second half of 2019. We used a deep-learning model that was trained with 296 images of 1000 × 1000 pixels distributed throughout the main oil-palm-producing regions and applied over Sentinel-1 and Sentinel-2 composites in the potential area (Fig. 2 ) where oil palm can grow. Table  1  shows the geospatial data used in the study. The links to the datasets appear in Sect. 6. The processing steps depicted in Fig. 1  were implemented in different computing environments (Appendix Fig. A1 ) depending on the convenience of the processing. The annual compositing of Sentinel-1 and Sentinel-2 images was done in Google Earth Engine (GEE)  (Gorelick et al.
, 2017)  since a cloud-processing platform was suited for this task considering the high amount of satellite data required in the compositing. The visual interpretation of training and validation data was also done in GEE. The training of the CNN and the classification of images, however, was performed with a local computer using Matlab 2019a since the implementation of the CNN model was less feasible in GEE. The CNN model can also be trained and used for the prediction of images with Python (code accessible through Sect. 5). The Sentinel-1 and Sentinel-2 images taken in 2019 are the only data necessary to reproduce the results of the global oil palm map. The rest is auxiliary data used for the identification of the oil palm distribution, the visual interpretation of oil palm plantation, and the comparison with other oil palm maps.
Potential distribution of oil palm The classification of oil palm plantations was restricted to those areas where the climatic conditions were favorable for oil palm growth. In order to delimit the potential distribution of oil palm, we used climate data and an existing global oil palm dataset. The climate dataset was obtained from World-Clim V1 Bioclim  (Hijmans et al. , 2005) , which provides 19 gridded variables at a spatial resolution of 30 arcsec that are generated from monthly temperature and precipitation. This study's existing oil palm layer was obtained from the Interna-tional Union for Conservation of Nature (IUCN;  Meijaard et al. , 2018)  and shows the industrial oil palm plantations at the global scale (link to the IUCN layer is available in Sect. 6). This map was derived from a compilation of all published spatial data on oil palm combined with the manual digitization of characteristic spatial signatures of industrial-scale oil palm using cloud-free Landsat mosaics acquired in 2017 and created in GEE.
The potential area where oil palm can grow was estimated with the climate variable range in the IUCN layer. We estimated the histogram of the 19 bioclimatic variables in the areas that were classified as industrial oil palm plantations in the IUCN layer. Appendix Table  A1  shows the minimum and maximum of each bioclimatic variable for the industrial plantations. A pixel in the WorldClim dataset was considered favorable for oil palm growth when at least 17 out of the 19 bioclimatic variables fell within the climate range observed in the IUCN layer (Appendix Fig. A2 ). The resulting potential oil palm distribution map encompasses similar areas as used in previous studies  (Pirker et al. , 2016; Strona et al. , 2018; Wich et al. , 2014). The classification of oil palm plantations was processed in a grid of 100 × 100 km that covers the area with favorable conditions for oil palm growth (Fig.
Sentinel-1 and Sentinel-2 preprocessing The CNN classifies radar and optical images collected by Sentinel-1 (C-band)  (Torres et al. , 2012)  and Sentinel-2 (multispectral)  (Drusch et al. , 2012)  satellites, respectively, both of which missions were launched by the European Space Agency and were part of the Copernicus Programme (https: //www. copernicus. eu, last access: 17 March 2021). The images were preprocessed and downloaded from GEE (code is available in Sect. 5,  Descals, 2021). We used the Sentinel-1 synthetic aperture radar (SAR) Ground Range Detected (GRD), which has a temporal resolution of 12 d, in both ascending and descending orbits. We used the Interferometric Wide Swath images processed at a spatial resolution of 10 m. The scenes were processed with the local incident angle (LIA) correction, and then the median value was computed over the second half of 2019 for the ascending and descending scenes separately.
pretation. We digitized the oil palm plantations also by interpreting the very high-resolution DigitalGlobe images that are displayed as the base layer in GEE. The DigitalGlobe images have a sub-meter spatial resolution and are displayed as true-color composites in GEE. These images are updated regularly, and the date depends on the location, but usually the images were taken during the past 1 to 2 years. The Dig-italGlobe images were used as complementary data to the Sentinel-1 and Sentinel-2 composites in the visual interpretation. We used the geometry editing tool in GEE for labeling smallholder and industrial plantations. Once the training areas were labeled, we downloaded the truth images from GEE along with the Sentinel-1 and Sentinel-2 composites for the second half of 2019. The image labeling was carried out in 84 different regions of the world where oil palm is cultivated (Appendix Fig.
A3 ) and resulted in 200 training images. Deep-learning algorithms require large amounts of data to ensure good performance, and data augmentation is a technique used to improve the performance of the models when the size of the training data is small  (Shorten and Khoshgoftaar, 2019). Data augmentation aims to generate a more diverse training dataset with certain affine transformations applied to the original training data. Data augmentation techniques have been used in remote-sensing studies  (Yu et al. , 2017) , in which affine transformations, such as flips, translations, and rotations, have improved the accuracy results of deep-learning models. We used the rotation of images (90 • clockwise) as the data augmentation technique for this study (Appendix Fig. A4 ). The rotation was applied only to the training images that presented more than 10 % of the pixels labeled as smallholders in order to reduce the class imbalance between industrial and smallholder plantations.
The reported metrics are the overall accuracy (OA), the user's accuracy (UA), and the producer's accuracy (PA). The accuracy metrics are reported with a confidence interval (95 % confidence level). Global OP Global OP (Sumatra)  Descals et al. (2019)   This study employed the classification model DeepLabv3+  (Chen et al. , 2017 (Chen et al. , , 2018) )  with the Mo-bileNetV2  (Sandler et al. , 2018)  as a backbone network. DeepLab has a series of versions for semantic segmentation. DeepLabv3+ is the latest version (link to the code in Sect. 5). The model uses an encoder-decoder architecture in which the image is downsampled with max-pooling layers during the encoder part and spatial information is retrieved during the decoder part. A characteristic of DeepLabv3+ is that the CNN uses atrous convolutions which enhance the field of view of filters to incorporate a larger spatial and informational context.
The points were visually interpreted using the Sentinel-1 and Sentinel-2 annual composites of the year 2019 (See Sect. 2. 3) and the DigitalGlobe orthoimages (<1 m spatial resolution) that are displayed as the base layer in the GEE code editor. The accuracy metrics that we reported were the overall accuracy (OA), the user's accuracy (UA), and the producer's accuracy (PA)  (Olofsson et al. , 2014). The OA is the proportion of reference points that have been correctly classified and is calculated by summing the number of correctly classified points and dividing by the total number of points. The OA represents the probability that a randomly sampled pixel is correctly classified. The PA results from dividing the number of correctly classified points in each class by the number of visually interpreted points for each class.
The area estimate and its confidence in-terval, however, cover the actual area with a given confidence level. Owing to the high imbalance in the validation dataset, we tested whether the overall accuracy of the CNN was higher than the no-information rate. The no-information rate was computed as the overall accuracy obtained if all pixels were classified as the major class, which is the class "Other land uses" in our study. The hypothesis test evaluates whether the overall accuracy obtained in the CNN classification is significantly higher than the no-information rate with a 95 % confidence level. If the null hypothesis is rejected (OA > noinformation rate), we can be assured that the CNN did better than predicting indiscriminately all pixels with the class "Other land uses".
Comparison with other oil palm datasets The accuracy of the CNN classification was compared with existing oil palm maps of Sumatra for the year 2019  (Descals et al. , 2019)  and Southeast Asia for the year 2016  (Xu et al. , 2020). Also, we compared our oil palm area estimates with the oil palm harvested area included in Food and Agriculture Organization of the United Nations (FAOSTAT) data at the country level and with the area estimates obtained from an oil palm map developed in  Gaveau et al. (2021)  over Indonesia for the year 2019. The oil palm maps in  Descals et al. (2019)  and  Xu et al. (2020)  were generated with a random forest classification, while the map developed by  Gaveau et al. (2021)  was generated by digitizing the oil palm plantations in Landsat and SPOT6 images. In order to compare the current results with our previous study in  Descals et al.
(2019) , we reclassified the young oil palm classes in this existing dataset to the class "Other land uses". We also kept only the validation points that cover Sumatra; this resulted in 2463 points out of the 13 495 total points. For the comparison with  Xu et al. (2020) , we used our CNN model to classify Sentinel-1 and Sentinel-2 composites for the second half of 2016. Moreover, we reclassified the smallholders and industrial plantations as a single class since the oil palm map in  Xu et al. (2020)  does not make distinctions between oil palm typology (industrial versus smallholder plantations). We also removed the validation points that were placed in young plantations because the temporal analysis in  Xu et al. (2020)  aimed to detect young oil palm and the plantations that had been clear-cut in the previous years.
(2020)  for 2016 (Appendix Table  A4 ). The producer's accuracy for industrial plantations in the results (PA = 76. 41 ± 3. 08 %) of  Xu et al. (2020)  is higher than our results (PA = 73. 65 ± 2. 94 %), although this difference is not significant for a confidence level of 95 %. The main difference between the datasets, however, was found in the user's accuracy for smallholders, in which our results excelled (UA = 96. 60 ± 0. 51 % compared to 57. 36 ± 3. 76 % in the dataset of  Xu et al. , 2020). The comparison with the data of  Xu et al. (2020) , however, only reflects the accuracies for closed-canopy oil palm plantations; the multi-annual analysis in  Xu et al. (2020)  also included the detection of disturbances in the time series to classify young plantations (Fig. 7 ). Similar to the data of  Xu et al.
(2020) , the dataset produced in  Gaveau et al. (2021)  also mapped young oil palm and areas that were clear-cut for oil palm plantation in Indonesia. For this reason, our closed-canopy oil palm area estimate was 12. 05 ± 0. 23 Mha in Indonesia -area mapped was 11. 54 Mha with 7. 71 Mha (66. 8 %) industrial and 3. 83 Mha (33. 2 %) smallholder -but, by comparison,  Gaveau et al. (2021)  found a higher oil palm area for Indonesia for the same year: 16. 26 Mha. Despite this difference,  Gaveau et al. (2021)  found a similar ratio between industrial and smallholder plantation extent: 10. 33 Mha industrial (64 %) and 5. 93 Mha smallholder (36 %). The comparison with inventories from FAOSTAT is also evidence of a large omission of oil palm plantations in West Africa (Appendix Fig. A7 ). The total surface reported as harvested area in FAOSTAT is 4.
We compared our findings with three studies, Descals et al. (  2019 ),  Xu et al. (2020) , and the dataset developed by  Gaveau et al. (2021). Our CNN model applied to Sentinel-1 and Sentinel-2 classified closed-canopy oil palm stands with higher detail (10 m spatial resolution) than existing datasets, although, at a coarser resolution (100 m spatial resolution), the temporal analysis used in  Xu et al. (2020)  aimed to detect disturbances (e. g. , land clearing and burning) that may or may not result in the development of oil palm plantations. Thus,  Xu et al. (2020)  classified open-canopy plantations that remained undetected in our classification. Accordingly, the omission error for oil palm was lower in the case of  Xu et al. (2020) , although this difference was not significant. However,  Xu et al.
Moreover, oil palm must be at least 3 years old  (Descals et al. , 2019)  to reach the full canopy closure. Therefore, it is likely that our maps missed young oil palm plantations developed after 2016. The dataset developed in  Gaveau et al. (2021)  is more suited to verify the impacts of the oil palm industry on forests, while our method is more suited to map the productive planted area, i. e. , closed-canopy oil palm stands >3 years old. In contrast, the dataset of  Gaveau et al. (2021)  was produced mostly by the visual interpretation and manual delimitation of oil palm development, while our method consisted of a supervised learning algorithm; our trained CNN can automatically classify remotely sensed data into oil palm maps for future land cover monitoring. Since our method classified only closed-canopy oil palm, it also struggled to detect oil palm in nonhomogeneous settings (e.
A8 ). The CNN model trained for the year 2019 is planned to be used for follow-up monitoring once a year to generate global oil palm maps. The shortcomings of deep learning include the high computational cost for training the models and the high cost for gathering labeled data compared to the standard machine-learning algorithms commonly used in remote sensing, such as random forest. In this study, 296 images of 1000 × 1000 pixels were used as a training dataset, consisting of 200 labeled images and 96 augmented images, and the computing time for training a pretrained DeepLabv3+ was nearly 8 d with an office computer. Despite this, the computing time and the size of our training dataset were considerably lower than state-of-the-art deep-learning studies in computer vision (i. e. , more than 200 000 labeled images in the Common Objects in Context, COCO, dataset) in which the number of classes and complexity of the classification problem surpasses the current study.
Data availability The dataset presented in this study is freely available for download at https://doi. org/10. 5281/zenodo. 4473715 (Descals et al. , 2021). The dataset contains 634 100 × 100 km tiles covering areas where oil palm plantations were detected. The file grid. shp contains the grid that covers the potential distribution of oil palm. The file grid_withOP. shp shows the 100 × 100 grid squares with the presence of oil palm plantations. The classified images (oil_palm_map folder, in GeoTIFF format) are the output of the convolutional neural network based on Sentinel-1 and Sentinel-2 half-yearly composites. The images have a spatial resolution of 10 m and contain three classes: (1) industrial closed-canopy oil palm plantations, (2) smallholder closed-canopy oil palm plantations, and (3) other land covers and/or uses that are not closed-canopy oil palm.