dataesr/scientific-paragraphs-categorization huggingface dataset:
e. , MODIS, Sentinel-2, Landsat-8). This method initially generated the provisional classifications by change detection based on the bi-temporal NBR index. Then, Bayesian updating of a land cover algorithm was used to combine the results of the burned classification maps, which were derived from multi-sensors. Finally, Belenguer-Plomer, et al. [47]  designed a deeplearning-based framework for the mapping of burned areas using Sentinal-1 and Sentinel-2 imagery. They used the multi-temporal γ0 of the Sentinel-1 dataset and the multitemporal spectral indices of the Sentinel-2 dataset as inputs for convolutional neural networks (CNNs). Although many studies have designed several algorithms for mapping burned areas and have developed them to specific types of satellite imagery (e. g. , MODIS, Landsat, VIIRS, Sentinel), many limitations remain, as follows: (1) Most methods have used the active fire products, while there is strong uncertainty in mapping active fires.
Moreover, the accuracy of the methods mentioned depends absolutely on the quality of the hotspot maps, and the hotspot products generally have low spatial resolution that might not be detected by the algorithm. (2) Spatial resolution is the most important challenge among these methods. However, coarse spatial resolution for mapping burned areas over wide coverage is not of great importance, but for small areas it is the most important factor. Therefore, mapping of burned areas for small areas using coarse resolution datasets is a big challenge, and can even be impossible. (3) Change-detection-based methods require more consideration because the changes can be derived from other factors, such as vegetation growth, tree felling and agricultural harvesting. Moreover, due to some environmental conditions (e.
g. , cloudy) and poor temporal resolution, finding a pre-event dataset can be very difficult. (4) Most methods are based on manual features generation based on spectral features (e. g. , NBR, NDVI), while they ignore the potential of spatial features in the mapping of burned areas. Additionally, manual features generation and selection of suitable features is time consuming, especially for large-scale areas. (  5 ) Most method have used conventional classifiers, such as random forest or maximum likelihood classifiers, while advanced classifiers can guarantee that reliable results will be obtained. These above-mentioned methods are based on thresholding spectral indices or original spectral bands. A single threshold value is, however, not effective, due to the complexity of the background and the ecosystem characteristics.
To minimise these challenges, the present study presents a novel framework for mapping burned areas. This framework can cover the limitations of current burned-area mapping methods. This proposed method is termed the QMDNN-Net method, and it is applied based on the fusion of Sentinel-1 and Sentinel-2 datasets, which have many advantages compared to other state-of-the-art methods. The QMDNN-Net method combines multi-scale convolution blocks and learnable morphological layers, and it can extract the linear and non-leaner deep features to improve the results of burned-area mapping in more complex areas. These advantages mean that the QMDNN-Net method can map burned areas with high accuracy and low error rates. Also, the efficiency of the fusion of the synthetic aperture radar (SAR) and optical datasets into a single dataset gains the maximum advantage of the fusion datasets.
The efficiency of morphological scale-space operators to increase the generalisation of deeplearning methods has been shown. Recurrently, the morphological neural network is taken into consideration, as this can provide promising results for some applications. Here, we combined the quadratic morphological operations with a standard convolution layer to enhance the performance of the network for solving non-linear burnedarea mapping in highly complex areas. The main contributions of this research are: (1) We deeply revisit WFBM methods and their problems, and propose a novel deep-learning framework that combines the morphological learnable scale-space operations (i. e. , quadratic dilation, quadratic erosion) and the groupdilated-convolution (GDC) block for the first time; (2) We introduce a quadratic morphological empirical mode (QMEM) based on two morphological operators for greater exploration of deep features; (3) We take advantage of the temporal coherence map and post-event optical datasets for accurate mapping of burned areas; (4) We evaluate the potential of the multispectral and multi-temporal coherence SAR datasets for mapping of burned areas, and compare the results with state-of-the-art methods.
B. Satellite Images This study used the post-fire Sentinel-2 dataset for mapping the burned areas. The Sentinel-2 mission consists of two satellites (Sentinel-2-A, Sentinel-2-B) that collect data with a temporal resolution of 5 days. The Sentinel-2 sensors have 13 spectral bands with different spatial resolutions  (10, 20, 60 m). This study used the spectral bands with spatial resolution of 10 m and 20 m. Furthermore, the Level-2A product was used for the next analysis that was preprocessed (i. e. , atmospheric correction). The Sentinel-1 satellites are equipped with C-band SAR that captures the data with four operational imaging modes of different spatial resolutions. Sentinel-1 has two sensors (Sentinel-1A, Sentinel-1B) that were launched in April 2014 and April 2016 (respectively). This study used the multitemporal single look complex with interferometric wide swathe mode for the WFBM.
C. Datasets This study fused the multi-temporal Sentinel-1 SAR dataset and the post-fire Sentinel-2 optical dataset for mapping of the burned areas. The Sentinel-1 datasets needed to be preprocessed to generate the multi-temporal coherence map. We used the SANP open-source software for preprocessing the Sentinel-1 data, to produce the coherence map. After preprocessing, and according to the flowchart presented in Fig. 4 , the multi-temporal coherence maps generated two polarisations (VV, VH). Fig. 2  and Fig. 3  show the incorporated Sentinel-2 dataset and multi-temporal coherence maps generated for the first and second datasets, respectively. This study was conducted experimentally in two different areas with wide coverage. The details of the incorporated datasets for the two study areas are presented in Table  I.
A. Preprocessing The preprocessing is the first step in the QMDNN-Net method, which is applied separately to the Sentinel-1 and Sentinel-2 datasets. The most important preprocessing of the Sentinel-2 dataset is an atmospheric correction to convert top-of-atmosphere to surface reflectance. The atmospheric correction was carried out using the Sen2Cor processer that is available in the open-source SNAP software  [48, 49]. The Sentinel-1 interferometric wide swath mode acquisitions in the single look complex product format were used for the WFBM. This product has several main steps of preprocessing: TOPSAR-spilt; apply orbit file; backgeocoding; enhance spectra diversity; coherence generation; TOPSAR-deburst; and multi-look and terrain correction  [50, 51]. The Sentinel-1 data processing is carried out via the SNAP software platform.
The complex coherence quantifies the relative stability of the scattering mechanisms within a window between two SAR acquisitions [52]. The complex coherence (γ) is between 0. 0 and 1. 0, and for two coregistered complex signals of SAR images it is estimated using Eq  (1)  [50, 53]: 𝛾 = 𝐸〈𝑠 1. 𝑠 2 * 〉 √𝐸〈|𝑠 1 | 2 〉. 𝐸〈|𝑠 2 | 2 〉 (1) where  1 and  2 are complex signals of the SAR datasets, 〈. 〉 is the expectation operator, * indicates the complex conjugate, and |. | indicates the absolute operator. In practical applications, we use the coherence ensemble average ( ̂) instead of the measurement of the mathematical expectation of SAR signals, which can be defined as in Eq(2): 𝛾 ̂= |∑ 𝑠 1𝑖 𝑁 𝑖=1 • 𝑠 2𝑖 * • 𝑒 𝑗𝜑(𝑖) | √∑ |𝑠 1𝑖 | 2 𝑁 𝑖=1. ∑ |𝑠 2𝑖 | 2 𝑁 𝑖=1 ( 2 ) where N is the number of pixels in the window.
H. Accuracy Assessment This study assessed the result of the WFBM using numerical and visual analyses with a reference dataset. Due to the role of the reference map in assessing the result of WFBM, this study was designed to generate the reference map with high reliability. The reference data was generated by a native expert based on visual interpretation of the very high resolution data and WFBM reports presented on some websites. Standard accuracy assessment indices were used for numerical accuracy assessment, which include: overall accuracy, kappa coefficient, F1-score, recall, Jaccard index, precision and balanced accuracy. To evaluate the performance of QMDNN-Net, a novel state-of-the-art deep-learning-based method was also implemented. This is a CNN-based method that was proposed by Belenguer-Plomer, et al.
IV. EXPERIMENTS AND RESULTS The QMDNN-Net method is applied in the supervised learning framework that is required to sample data. We collected the sample dataset as randomised and systematic. In addition, Table  II  shows the details of the incorporated sample data for the two study areas. It can be noted that the collected sample data are <1% of the whole dataset. The sample data is divided into three parts: the training, validation and testing data, as 65%, 15% and 20%, respectively, of the whole sample dataset.
A. Parameter Settings The parameter  is defined by trial-and-error, which is here set to 4. 0. The QMDNN-Net method has hyperparameters that it needs to set, which are set manually based on trial and error. The optimum values of these parameters are set as follows: input patch-size for Sentinel-2 and Sentinel-1 multi-temporal coherence datasets, 13×13×10 and 13×13×8, respectively; initial learning rate 10 -4 ; number of neurons at the fully-connected layer, 1500; mini-batch size, 900; and number of iterations, 1500 (epochs). It is worth noting that the effects of these parameters on the performance of the network are low; e. g. , the change in patch size of 11×11 to 13×13 only increases the accuracy of the network by 0. 5%.
1) First Study Area The results of the WFBM for the first study area are shown in Fig. 10. As can be seen, the QMDNN-Net method showed good detection (Fig. 10b , and Fig. 10c ), and it is clear that other CNN-based method has high false pixels (Fig. 10a ). While this issue can also be seen in the results of the QMDNN-Net method, this effect is lower than for the other methods (Fig. 10c ). The QMDNN-Net method also provides considerable improvement in the WFBM by fusion of the multi-temporal Sentinel-1 coherence map and the Sentinel-2 optical imagery, which relates mainly to around the burned areas. This improved efficiency is more evident in the detection of the Unburned pixels that can be seen for the whole study area. 10. Among the 3,555,268 total ground truth Unburned pixels, the QMDNN-Net method detected 3,193,267 and 3,427,761 Unburned pixels for only the Sentinel-2 dataset and for the Sentinel-1 and Sentinel-2 fusion dataset, respectively, while the CNN-based method detected 3,170,464 Unburned pixels.
Thus, in the fusion scenario, the QMDNN-Net method has the lowest Unburned error pixels, at 127,507, compared to the CNN-based method with 384,804 error pixels. It is also worth noting that for the 5,425,604 total ground truth Burned pixels, the CNN-based method shows 5,160,187 Burned pixels, while for the fusion scenario, the QMDNN-Net method provides fewer, at 5,088,568. Thus the CNN-based method provides better performance compared to the QMDNN-Net for the detection of Burned pixels, while it significantly misses its performance in the mapping of the Unburned pixels. Comparison of the confusion matrices for the CNN-based method of  [47]  (a) and the proposed QMDNN-Net method using just the Sentinel-2 dataset (b) and using the Sentinel-1 and Sentinel-2 fusion dataset (c). The numerical results of the mapping of the burned areas in terms of the quality measurement indices for the first study area are presented in Table  III.
These show that all of the methods provide an overall accuracy >92%. The QMDNN-Net method outperformed the CNN-based method according to most of the indices, using either only the Sentinel-2 dataset or the Sentinel-1 and Sentinel-2 fusion dataset. The QMDNN-Net method improves the performance of the burned-area mapping by >2% according to the overall accuracy index. Furthermore, there is an improvement in most of the other indices, such as the Recall index, kappa coefficient and Jaccard index. However, the CNN-based method provides greater precision compared to the QMDNN-Net method, although it does missed its performance for other indices. Additionally, the QMDNN-Net method generally provides improved performance with the fusion of the multi-temporal coherence images and the optical Sentinel-2 datasets.
2) Second Study Area The result of the WFBM for the second study area are shown in Fig. 12 , where it can be seen that all of the methods provide good results for detection of the burned areas. The main challenge among the methods is for the correct mapping of the unburned pixels. There are many pixels that are classified as Burned pixels when they should be Unburned pixels. The CNN-based method shows many false pixels around the burned region. However, the QMDNN-Net method shows good detection compared to the other methods for both of its scenarios. The QMDNN-Net provides good performance in the second scenario (Fig. 12c ), while some noisy labelled pixels can be seen in the first scenario (Fig. 12b ). The confusion matrix of the results for the second study area is shown in Fig. 13. Here, the QMDNN-Net method shows better efficiency for classification of the Unburned pixels, where it correctly defines more than 39,369,089 pixels from 40,186,532 total ground truth pixels using only the Sentinel-2 dataset.
Furthermore, the QMDNN-Net method improved upon this performance of detection of the Unburned pixels in the fusion dataset (i. e. , 39,374,292 Unburned pixels correctly identified). However, the CNNbased method provides some improvement in the detection of the Burned pixels, although this benefit is small in comparison with the error pixels in the Unburned class. The quality indices for these numerical results for the WFBM methods for the second study area are presented in Table  IV. Here, the two methods provide overall accuracies of >93%, although the QMDNN-Net method provides greater accuracy than the CNN-based method for most of these indices. Indeed, the QMDNN-Net method improves the accuracy mapping by >4% in terms of overall accuracy and the Recall index. Furthermore, there is a significant improvement for the WFBM using the QMDNN-Net method according to the kappa coefficient.
As can be seen, using the Sentinel-1 and Sentinel-2 fusion dataset, the QMDNN-Net method has higher efficiency than the Landsat-8 burned-area product. Indeed, the Landsat-8 burned-area product shows an overall accuracy as low as 60% (first study area), while the QMDNN-Net method has a corresponding overall accuracy of >94%. Thus, while the Landsat-8 burned-area product shows higher efficiency for mapping the Unburned areas than the QMDNN-Net method, its performance is greatly reduced by the lower efficiency for mapping the Burned areas. Considering the balanced accuracy and F1-score as particularly important factors to define the results here, it can be seen that the QMDNN-Net method is significantly higher for both of these scores, compared to the Landsat-8 burned-area product. Accurately and timely detection of burned areas are the most important factors in WFBM damage assessment.
Furthermore, the selection of a suitable threshold for discriminating the burned areas is challenging. Burned-area mapping by machine-learning methods has become more popular recently. The main advantages of machine-learning methods are the relatively low requirement for sample data and the ease of implementation. The strength of these methods depends on the quality of the input dataset and the features generated. Table  VI  gives an overview of WFBM using machine-learning methods. Most of these methods provide an overall accuracy of around 92% for small areas, while the QMDNN-Net method provides an overall accuracy of >94% for large-scale areas. Additionally, the generation of efficient spatial and spectral features is time-consuming, especially for large-scale areas. It is worth noting that  [70]  was provided the accuracy similar to proposed method but this framework is based on manual feature extraction and thresholding.
The QMDNN-Net has high efficiency in the detection of unburned pixels by the first scenario (S#1). Furthermore, the QMDNN-Net method has provided the considerable performance in the detection of burned pixels while miss the efficiency in the detection of unburned pixels (false pixels are more than 1,540,000 pixels). Thus, the QMDNN-Net has improved the result by combining the QM-block and GDCblock as it reduces the miss detection and false pixels. The results of the ablation analysis for the second dataset in three scenarios are shown in Table  VII. As seen, the performance of QMDNN-Net is better than other two scenarios by the most accuracy assessment indices (i. e. overall accuracy, Recall, F1-score, Jaccard index, and Kappa coefficient). It is worth noting that the proposed method has provided the accuracy a bit better than both scenarios by Precision and Balanced accuracy but it considerable miss the performance by other accuracy assessment indices (i.
e. Recall, Jaccard index, and Kappa coefficient). There is however a trade-off between detecting burned and unburned samples that the ideal case is when a model can effectively classify both classes with minimum classification error. This study proposes a novel framework based on hybrid QM layers and GDC-blocks for mapping burned areas. Based on the results presented here for two large-scale areas, the proposed QMDNN-Net method outperforms the other state-of-the-art deep-learning-based WFBM methods. The strength of the QMDNN-Net method is its use of GDC-block and morphological layers that can detect burned areas with high complexity. This QMDNN-Net method is additive with high-resolution datasets that help the accurate mapping of burned area. The QMDNN-Net method combines the Sentinel-2 and multi-temporal Sentinel-1 coherence dataset for the mapping of burned areas, which improves the efficiency of this WFBM by >2% according to the overall accuracy index.
One of the main advantages is that the QMDNN-Net method requires few sample data, as the sample data used represents <1% of the whole of the dataset. However, semantic segmentation-based methods (e. g. , U-Net, DeeplabV3+)  [13, 35]  have provided promising results for many applications, although it is worth noting that these require large sample datasets. The collection of sample data is the most challenging aspect of supervised learning methods, and especially for WFBM. Furthermore, these methods need more time for the tuning of their parameters, as this is time-consuming and challenging. The fusion of remote sensing datasets by deep-learning methods can be applied at different levels, such as shallow feature levels and high feature levels. It has been shown that high feature level fusion can improve the efficiency of deeplearning-based methods.
To this end, this study proposed a novel framework for fusion at a high deep-feature level based on a double stream deep-feature extractor. The Sentinel-2 and multi-temporal coherence images are investigated using these channels, and then they are fused in the latest layers by concatenation. The double stream led to the extraction of a high-level feature map from the original dataset that uses the full potential of each of the datasets. The generalisation capacity is the most important factor among the deep-learning-based methods. Unlike many deeplearning-based methods, the QMDNN-Net method has high generalisation capacity due to its structure. For example, there is a waterbody area and agricultural farms in the second dataset in the present study that are not included in the sample datasets.
VI. CONCLUSION Accurate and timely WFBM can help damage assessment and better management of such disasters. In this study, we propose a novel framework for WFBM that combines quadratic morphological layers and convolution layers. Postevent Sentinel-2 and multi-temporal coherence images are used for this burned-areas mapping for two large-scale regions. The performance of the QMDNN-Net method is compared here with other state-of-the-art methods and some burned-area products. The results of this WFBM shows that the proposed QMDNN-Net algorithm has high efficiency in the mapping of burned areas. Furthermore, the fusion of post-event Sentinel-2 images with multi-temporal Sentinel-1 coherence datasets can improve the results of this WFBM and reduce the false pixel levels. The fusion of the Sentinel-1 and Sentinel-2 datasets also helps to map the burned areas in more detail and with more precision.
Based on visual and numerical analyses, the burned-area products originated from MODIS and the Landsat-8 burned-area products show high missed detection of pixels and low accuracy in comparison with the Sentinelbased results. Furthermore, they have low spatial resolution, while the QMDNN-Net method has high spatial resolution. The QMDNN-Net method has several particular benefits compared to other burned-area mapping methods: (1) it provides high accuracy (>95%) in the mapping of burned areas; (2) the fusion of the Sentinel-2 and Sentinel-1 coherence datasets provide a high feature level based on double deep-feature extractor streams; (3) it requires relatively few sample data; (4) it combines the morphological and convolutional layers, which increases the efficacy of the network for WFBM in highly complex areas; and (  5 ) it has high generalization capabilities over various study areas.
index CNN-based method [47] QMDNN-Net datasets Sentinel-2 only Sentinel-1/2 fusion Overall accuracy (%) 92. 75 92. 83 94. 82 Precision 92. 27 91. 88 91. 05 Recall 89. 17 89. 81 96. 41 F1-score (%) 90. 69 90. 84 93. 65 Balanced accuracy 92. 14 92. 31 95. 10 Jaccard index 0. 829 0. 831 0. 881 Kappa coefficient 0. 847 0. 849 0. 892.
Microbial taxonomy and acquisition by founding reproductives are major drivers of inheritance We con rmed that vertical transmission by founding reproductives and ASV taxonomy strongly impact the probability of inheritance with a Random Forest (RF) model. The RF model assessed the predictive power of a set of experimental variables for the observed inheritance and the null model previously described, where the chance of vertical transmission was relative to ASV abundance. The experimental variables included bacterial taxonomy, abundance in maternal colonies, and network features. The predictive power of the RF model using the observed data was 2. 0 to 2. 5-fold higher than the null model, highlighting the impact of variables aside from abundance, such as microbial taxonomy (Figure  5A ). Consistent with founding reproductives shaping the inherited microbiome, transmission to founding reproductives was more predictable than transmission to offspring colonies (Figure  5A ).
DNA was extracted using a modi ed DNeasy Blood and Tissue kit protocol (Qiagen, Germany)  (Hu et al. , 2019). The guts of queens from parental colonies were divided into three extractions due to tissue size and later pooled in silico. Relative microbial loads per termite gut were determined by quantifying copies of the V2 region of the 16S rRNA marker gene using droplet digital PCR (BioRad, Denmark)  (Zhukova et al. , 2017). Non-template controls were over 100-fold less that the lowest quanti cation of termite gut extracts. In the nal analysis, any dilution for ddPCR, the total volume of the elute from DNA extraction, and the number of termites per DNA extract were corrected for to determine gene copies per gut (Figure  1  -source data 2). Amplicon sequencing, quality control, and taxonomic assignment DNA was sent to BGI-Hong Kong for paired-end HiSeq2500 amplicon sequencing with 341F/806R primers targeting the V3-V4 region of the 16S rRNA gene.