dataesr/scientific-paragraphs-categorization huggingface dataset:
Stratification The WorldCereal classification system aims for product generation within one month after the end of a particular growing season. Due to the dynamic nature of these growing seasons across the globe, we created a stratification based on the global crop calendars discussed in Sect. 2. 2. Regions sharing similar crop calendars were grouped into 203 homogenous agroecological zones (AEZ) that are used as a mapping trigger for the system (Figure  2 ). In addition,  Buchhorn et al. (2020)  reported that classification algorithms for global mapping purposes are better adapted to sub-continental and continental patterns if they are trained and applied at sub-global scale. Therefore, we also stratified the global terrestrial regions into large biogeographical realms (Figure  3 ) following the Ecoregions2017 dataset  (Dinerstein et al.
Feature extraction Classification features were derived from the five data sources discussed in Sect. 3. 2, i. e. optical, radar, thermal infrared, DEM and fuzzy biomes. Aside from the DEM and biomes, feature extraction always starts from pre-processed time series, either directly derived from the data source (e. g. Sentinel-2 reflectance bands) or a derived time series using a combination of multiple input variables (e. g. a spectral index). Exact timing and length of the time series was determined by the pixelbased crop calendars for the respective products (Sect. 2. 2Error! Reference source not found. ). Whereas different features were originally computed at the native spatial resolution of the input data source for computational purposes, in the end all features were resampled to 10m resolution before being used as input in the classification models.
These divisions were made to ensure that there is no bias toward regions with longer growing seasons. Finally, to also include the relation between soil moisture and irrigation, the optical trapezoid model (OPTRAM) was used. This model focuses on the relationship between shortwave infrared reflectance and the NDVI  (Sadeghi et al. , 2017). In this model, the shortwave infrared reflectance is converted into surface-transformed reflectance (STR). A trapezoidal model relies on a predefined wet and dry edge. These edges explain at which NDVI and STR value the soil is saturated or at its have caused the increase in soil moisture content. For this dataset also p50, p90 and STD were calculated. Table  3  shows an overview of all the features used in the irrigation classification algorithm.
Annual temporary crop maps We followed the guidelines for rigorous accuracy assessment provided in  Stehman and Foody (2019)  and  Szantoi et al. (2021). To validate the annual temporary crop products, a new validation dataset  (Lesiv et al. , 2023b)  was created which is completely independent from all other existing maps or reference datasets, and which is in line with the cropland definition and mapping period of the WorldCereal products as outlined in Sect. 2. The sampling design of the validation dataset is probabilistic with random distribution of sample sites. The validation sample sites were generated before the WorldCereal products were produced. Therefore, to avoid issues with inclusion probabilities, we selected a random sample design in equal area projection (Goode Homolosine).
Taking into account that arable land covers approximately up to 10% of all land, the sample size consisted of 50 000 unique sample sites, with possibly up to 5000 sample sites labeled as temporary crops. Response design has been implemented in the Geo-Wiki application  (Fritz et al. , 2012)  where each validation sample site has been visually interpreted by several experts. To decide if a sample is covered by temporary crops in a given period of time, the experts looked at very high-resolution Google historical imagery and Google Street level images, Microsoft Bing images, ESRI imagery, Planet historical data, Sentinel-2 time series, and Modis NDVI time series. The validation sample sites where the experts disagreed on temporary crop presence were revisited and revised. By using the new validation dataset, we calculated confusion matrices with accuracy metrics such as overall, user's and producer's accuracies.
Seasonal crop type maps The coverage and availability of crop type information for the year 2021 is limited. Therefore, to get a global overview of the quality of the crop type products, we decided to invest in a new crop type validation dataset  (Lesiv et al. , 2023a)  disadvantage is that users need to check plenty of images where only few will clearly show cropland fields that are mature enough to be identified. To make the data collection more efficient, we provided our experts with preliminary maps of points in agricultural areas where street level images are available for the year 2021. Then, the experts checked those locations in an opportunistic way. In total, we collected around 3500 unique locations, distributed around the globe and matching the WorldCereal 2021 mapped seasons (Table  4 ).
This dataset is completely independent from all the existing maps and reference datasets. Since it is not a subset from the training dataset, there are no potential issues related to spatial autocorrelation between training and validation datasets. Though, it has a limitationthe sample design is not probabilistic. By using the new validation dataset on crop type, we calculated a confusion matrix with metrics, which we called overall agreement and agreement by classes. We did not use the term "accuracy" since the sample design is not probabilistic.
Seasonal active irrigation maps The amount of total land that is being irrigated differs from year to year, heavily depending on weather conditions. As it has been mentioned in the description of training data, there is very limited information available about actual irrigation of cropland fields, giving us little means to run a quantitative validation of irrigation products as such, especially by season. For irrigation we hence focus on a qualitative assessment, by spatially comparing the WorldCereal irrigation products with two open access datasets: (i) Global map of areas equipped for irrigation expressed as percentages, produced by FAO  (Siebert et al. , 2013) ; and (ii) the Landsat-Derived Global Rainfed and Irrigated-Cropland Product at 30 meters (LGRIP30)  (Teluguntla et al. , 2023).
This could be explained by a lack of training data. Important to note is that the presented results are biased towards the areas covered by the validation dataset, which includes 2617 crop type records. • Wherever the WorldCereal irrigation products show less irrigation (areas highlighted in red), it could be that not all the areas equipped for irrigation were actually irrigated in 2021. This is a common practice in many countries. Also, both the FAO and LGRIP30 maps include perennial cropland in their definitions, while the WorldCereal products do not. Therefore, it is logical that those two maps show more irrigation areas in some places. Finally, as mentioned in the definition of the WorldCereal irrigation product, we do not consider incidentally irrigated cropland, for example cropland that is only irrigated during the sowing period, which could also be a cause of the general underestimation of irrigated land compared to other datasets.
• Wherever the WorldCereal irrigation products show more irrigation (areas highlighted in blue), those pixels could be either areas recently equipped for irrigation, or WorldCereal commission errors, or FAO's and LGRIP30 omission errors. However, we think that the hotspots in the south of the Sahel, the USA, Russia and Brazil are most likely WorldCereal commission errors. Resolution (GCEP30)  (Thenkabail et al. , 2021)  to mask agricultural areas and it combines multiple spectral bands and indices of Landsat-8 from 2014-2017 to train multiple machine learning models  (Teluguntla et al. , 2023). From the different irrigation maps, the map from  Siebert et al. (2013)  agrees best with the statistical datasets. Both the maps from Meier et al. The WorldCereal 2021 products are available at: https://doi.
org/10. 5281/zenodo. 7875105  (Van Tricht et al. , 2023). Each WorldCereal product has its own archive in the repository and contains cloud-optimized geotiff (COG) files per AEZ which were reprojected from the original Sentinel-2 tile grid to the lat/lon WGS84 projection. Confidence layers are available separately and were downsampled to 0. 0004 ° resolution.
Reference data The harmonized reference data used in the WorldCereal system can be accessed in two ways. The first way to find the data is using the Geo-Wiki hosted reference data module available at https://worldcereal-rdm. geo-wiki. org, where users can browse through the different datasets, visible on a global map. All data and metadata can also be downloaded from the website. A second way to access the data is by entering the WorldCereal community in the Zenodo data repository, available at https://zenodo. org/communities/worldcereal-rdm/. The repository shows the harmonized data in three parts, each one having its own license, based on the licence of the original datasets. Furthermore, the protocol to harmonize the reference data is also available there. The Zenodo repository also has API access enabled.
The new reference datasets developed for validation of the WorldCereal products are available on ▪ https://doi. org/10. 5281/zenodo. 7825628 for the global crop type collected using the Street Imagery validation tool  (Lesiv et al. , 2023a)  ▪ https://doi. org/10. 5281/zenodo. 7837480 for the global validation dataset on temporary crop 2021 collected using Geo-Wiki  (Lesiv et al. , 2023b).
The complete 2021 WorldCereal product suite can also act as a foundation for a worldwide crop monitoring system, providing a significant step forward in addressing the challenge of global food security. Due to these significant potential contributions, we want to strongly emphasize the importance of continuing the development of the system beyond this 2021 showcase. Moving forward, we recommend focusing on enhancing the quality of the products in areas where confidence is lowest by forging local/regional collaborations in improved collection of ground truth data which will further enhance the local applicability of these products. Such continued community efforts are crucial to support further improvements to the system and push the boundaries for global agricultural mapping from space.
Acknowledgements First and foremost, we would like to express our sincere gratitude to the  European Space Agency (ESA)  and the  WorldCereal Champion  users (FAO,  AMIS, GEOGLAM)  for their support in conceptualizing and throughout the WorldCereal project. A special thanks goes to  Benjamin Koetz ,  Giuseppe Ottavianelli  and  Amalia Castro Gomez  ( ESA ), Ian https://doi. org/10. 5194/essd-2023-184 Preprint. Discussion started: 24 May 2023 c Author(s) 2023. CC BY 4. 0 License. Jarvis  ( GEOGLAM ),  Jippe Hoogeveen  and  Livia Peiser  ( FAO Land and Water division )  Mario Zappacosta  ( FAO GIEWS ),  Lorenzo De Simone ,  Giulia Conchedda  and  Francesco Tubiello  ( FAO Statistics Division ) for their strong involvement. We are also very grateful to the many other individuals and organisations who provided us with their time, resources, and expertise throughout the project, we explicitly thank the teams of  EU-JRC-ASAP ,  NASA Harvest ,  UN-WFP  for their invaluable contributions, and feedback on the products.
05 and early stopping activated after 40 rounds without improvement. The distribution of the binarized training samples is imbalanced, the degree by which depends on the availability of different sources of reference datasets. To cope with this imbalance, we computed the class weights that balance the distribution, which we then used for loss weighting to eliminate the imbalance problem. In addition to these class weights, sample specific weights were also adjusted based on the confidence score of the respective reference dataset they were originating from (cfr. Sect. 3. 1). It is important to note that the models were trained on the combined training data from the available years (2017-2021) without providing year-specific information to the model. The aim was to train generalized models across multiple years that do not specifically require new training data in unseen years.
( 2018) and Wu et al. (2023)  show an overestimation of irrigated areas compared to the statistical datasets. The LGRIP30 map shows a drastic overestimation of the irrigated area, which is almost double compared to the findings from the CIA and ICID. Finally, the WorldCereal irrigation product underestimates the global irrigated area by roughly 35 %, which is partly due to omission errors, but also caused by the fact that the WorldCereal product only focuses on temporary crops. The year 2021 is deemed to be a relatively wet year for Europe, South America, Australia, and parts of Southern Asia (NOAA, 2022), so potentially many farms that are equipped for irrigation did not require irrigation. Producing more irrigated area maps for different years with the WorldCereal system should give more insight into this hypothesis.
Finally, to fully integrate the characteristics of high-frequency local and low-frequency global, the Assembled transformer module (ATM) is placed at the bottleneck of the network. The correlation between deformable features and features-among computation is characterized by introducing them into the above two modules of AMFFM and ATM. As the design goal and eventually demonstrated, BPSM and ATM promote the proposed BPAT-UNet to further constrain boundaries, whereas AMFFM assists to detect small objects. Results: Compared to other classical segmentation networks, the proposed BPAT-UNet displays superior segmentation performance in visualization results and evaluation metrics. Significant improvement of segmentation accuracy was shown on the public thyroid dataset of TN3k with Dice similarity coefficient (DSC) of 81.
Fully convolutional network (FCN) is the representative network of CNNs for image semantic segmentation  [6]. Compared with FCN, UNet plays a greater role in the field of medical image processing where data are relatively scarce  [7]. The key core is the skip connection that can transmit the low-level features to reserve local information. UNet-based networks have made significant progress in recent years. However, Fig. 1  shows that UNet architecture still suffers from two limitations in the task of thyroid ultrasound image segmentation. (1) Hard to deal with the disseminated boundary. The first row of Fig. 1  shows a typical example of thyroid ultrasound image segmentation. UNet segmentation results fail to maintain accurate boundaries. The reason is that the gray value of the pixels on the edge of thyroid nodules is usually very close to the surrounding pixels, which can easily lead to errors in automatic segmentation.
Res-UNet builds the powerful Resnet into a Ushaped network  [22, 23]. UNet++ chooses dense skip connections to capture fine details in the foreground  [24]. To detect the small objects, Dense-UNet fuses small-scale features within and between slices in a densely connected manner to enhance context  [25]. FactSeg improves the accuracy of small object segmentation by multi-scale fusion of two branches  [26]. To obtain the boundaries of the tissues more precisely, some studies add shape constraints to the network. Lee explicitly introduces the boundary point detection module in CNNs, which uses multi-scale atrous convolution to generate boundary point prediction maps  [11]. SAUNet also constructs a shaped flow and passes gradients to capture rich shape-related information  [27].
(2) The adaptive multi-scale features fusion module (AMFFM) is adopted to enhance local features. The deformable convolutional and deformable attention blocks are alternately used can better capture the characteristics of the nodules. (3) The Assembled transformer module (ATM) performs two self-attention routes to process global and local information for small nodules detection. The Extern attention (EA) block can learn the underlying correlations sample-among of the dataset. (4) According to the quantitative results on the thyroid public dataset TN3k  [20]  and our private dataset, the proposed approach displays significant accuracy advantages compared with other state-of-the-art techniques. The rest of the paper is organized as follows. In Section II, we describe the architecture of the proposed BPAT-UNet and its modules in detail for thyroid nodule segmentation.
Overall structure design The UNet framework mainly consists of an encoder, bottleneck, decoder, and skip connections, where both encoder and decoder are based on Double convolution blocks (DCB). The traditional UNet consists of multi-scale layers, where n indicates the n -th layer of the network, n = 1 , 2 , 3 , 4 , 5. To effectively maintain the shape of the irregular nodule's boundary and small object features, we augment three important modules into the UNet framework. The BSPM is inserted into the 3 rd, 4 t h and 5 t h layers for boundary point feature supervision. The AMFFM is inserted into the 3 rd and 4 th layers for feature aggregation. The ATM is inserted into the bottleneck for samples-among adaptation. For a given thyroid image X ∈ R H×W ×3 , the encoder performs multi-scale feature processing to obtain depth features.
Local features are obtained by windowed attention and convolution, whereas global features are obtained by standard self-attention. This behavior effectively learns comprehensive features that contain highfrequency and low-frequency information in visual data. Furthermore, we replace the Multi-layer perception (MLP) in transformer with External Attention, which can explore the interrelationships between different data samples. By replacing the ∈ R N× C 5 2 , respectively. They are calculated in two different ways. First, we propose a parallel architecture that mixes the deformable convolution and the windowed deformable attention to learn high-frequency and local components in detail. The purpose of introducing deformable feature calculation is to reduce the recognition problem of objects with large differences in shape.
Datasets To evaluate our model, we use two data sets: the public TN3k dataset and our own private thyroid dataset. (1) TN3k dataset : The TN3k dataset contains 3493 ultrasound images collected from 2421 patients, all of which are grayscale. We chose 2303 images as the training set, leaving 576 images as the validation set, and an additional 614 images for testing the best model. They are resized to 256 × 256 and we performed data expansion such as normalization and random inversion. ( We divide the datasets in a ratio of 8:1:1 for training, validation and testing. The delineation of the nodules was performed by three physicians with extensive clinical experience in the ultrasound department. We convert contours to contours and binary masks as ground truths. To remove patient privacy and other irrelevant information, we crop the images and labels to 512 × 512.
Implementation details The network BPAT-UNet was built with PyTorch 1. 7. 0, using the Adam optimizer with a weight decay of 1 e -4. The initial learning rate is set to 1 e -4 , and the learning rate is decayed by a warmup cosine annealing algorithm. All experiments are performed on Tesla V100 32G with batch size set to 16 and a maximum of 150 epochs. We use the weights of the TN3k dataset for transfer learning by considering the small number of private thyroid datasets to avoid over-fitting.
Results on the TN3k dataset Firstly, we evaluated these methods on the public TN3k dataset. Fig. 6  presents the segmentation results on five example images of the testing dataset obtained by the proposed BPAT-UNet and the EIGHT other methods aforementioned. The segmentation results provided by these methods are displayed on the third to the tenth columns, respectively. The first to sixth lines in Fig. 6  present the segmentation results of six patients. Obviously, the proposed BPAT-UNet is the most effective to detect thyroid nodules from ultrasound images visually. Our BPAT-UNet has a superior perception ability for small thyroid nodule objects compared with other models, from the segmentation effect of small objects in the first three rows of Fig. 6. Thus that our AMFFM compensates for the smaller features that are easily lost in downsampling.
Results on the private dataset Second, we evaluated these methods on our own private thyroid dataset. Fig. 7  presents the segmentation results on three sample images of the testing dataset obtained by the proposed BPAT-UNet and the EIGHT methods aforementioned. The segmentation results provided by these methods are displayed on the third to the tenth columns, respectively. The first to fifth rows of Fig. 7  present the segmentation results of five patients. Also, the proposed BPAT-UNet is the most effective in detecting thyroid nodules from ultrasound images visually. Compare with other methods, it is found that the proposed BPAT-UNet can identify non-nodule regions from the thyroid ultrasound images. Similar to the segmentation performance of TN3k dataset, the segmentation prediction results show that for dense and small-scale objects, our BPAT-UNet performs significantly better than other models for thyroid ultrasound image segmentation.
Results on TN3k dataset Table  2  and Fig. 8  present the experimental results in two different forms on the TN3K dataset. Quantitative results show that our proposed BPAT-UNet achieves superior segmentation performance on evaluation metrics compared with other schemes, scoring 71. 87 % in IoU, 83. 64 % in DSC, 14. 06 in HD95, 84. 23 % in F1-Score, 97. 22 % in Accuracy, 92. 03 % in AUC, 85. 57 % in Recall and 82. 94 % in Precision. Compared to baseline method UNet, BPAT-UNet yields 4. 1 % , 2. 85 % , 7. 45, 2. 61 % , 0. 6 % , 0. 44 % , 0. 5 % , and 4. 5 % improvement in IoU, DSC, HD95, F1-Score, Accuracy, AUC, Recall and Precision. As a result of the numerous indicators, we mainly compare multiple methods for representative indicators such as DSC and HD95. For DSC, the proposed BPAT-UNet reaches the score of 83.
58), which is an improvement of 4. 52 compared with FCN (18. 58), which is an improvement of 4. 99 compared with AttnUnet (19. 05), which is an improvement of 5. 24 compared with UNet Transformer  (19. 30) , which is an improvement of 2. 77 compared with UT-Net (16. 83), which is an improvement of 4. 75 compared with TransUnet (18. 81), which is an improvement of 1. 52 compared with SmaAt-UNet (15. 58). From the DCS and HD95 results, we find that UNet achieves the ideal segmentation effect on the TN3k dataset as it integrates the features from the underlying space through a multi-scale structure and skip connection. FCN slightly outperforms UNet based on the pre-training weights. Although DeepLab V3+ has a well-designed atrous spatial pyramid pooling and decoder structure, it is not ideal for the thyroid TN3k dataset.
In the scheme of adding the attention mechanism to convolution, AttnUNet achieves a high DSC score. By adding channel attention to convolution, SmaAt-UNet achieves the second-highest DSC score. With the help of the introduction of the Transformer into the CNN, the problem that the convolutional receptive field has limited effect and is difficult to capture the long-range context is solved. UT-Net introduces residual self-attention to form a residual Transformer that slightly improves the DSC score. UNet Transformer adds self-attention and cross-attention to bottleneck and skip connection, respectively, increasing the global receptive field, and has achieved minimal performance improvement. TransUNet combines the convolutional layer and the Transformer layer in turn achieves good results. Similarly, our BPAT-UNet inherits Transformer's excellent global modeling capabilities and has excellent DSC metric results on the TN3k dataset.