dataesr/scientific-paragraphs-categorization huggingface dataset:
However, the usage of PlanetScope data does not come without limitations. For instance, the inter-calibration between the CubeSats adds uncertainties in the data analysis of surface reflectance  (Pandey et al. , 2021). This makes it difficult to create robust large-scale applications because traditional machine learning and time-series algorithms operate on a per-pixel basis and largely depend on high-quality reflectance values. One potential solution is the application of DL methods for semantic segmentation, and more specifically, based on convolutional neural networks (CNN)  (Lecun et al. , 2015). CNN methods are not constrained by specific values of a given pixel in the image, but they leverage the local textural variability as main sources of information to fit non-linear relationships at multiple levels of abstraction and map inputs into outputs with state-of-the-art accuracy  (Lecun et al.
, 2017). These numbers correspond to 43%, 52% and 32% of the whole Brazilian Amazon estimates, respectively. These estimates were based on the GFC data  (Hansen et al. , 2013)  and represent a sample-based approach to disentangle the effects of each degradation type  (Tyukavina et al. , 2017). A spatially explicit map attributing the different degradation types to every unit of area has yet to be developed and validated and would be very valuable for REDD+ and forest monitoring operations. The state of Mato Grosso represents a highly relevant study area for testing a degradation mapping approach, due to the availability of regional datasets such as SIMEX and DETER that can be used to further evaluate the model predictions. In this study, we develop and evaluate a methodology for mapping forest degradation with attributions in tropical rainforest ecosystems, using high spatial resolution satellite imagery from Planet NICFI and deep learning.
Forest degradation mapping methodology 2. 2. 1. Planet NICFI satellite data We acquired bi-annual surface reflectance satellite data from the Planet NICFI product ('analytic' mosaic) having red (R), green (G), blue (B) and near-infrared (NIR) bands and a nominal spatial resolution of 4. 77 m (NICFI, 2021). Each Planet NICFI quad (or tile) corresponds to 20 × 20 km in the ground and has 4096 × 4096 pixels. A total of 1530 tiles covered the study area (delimited by the red boundaries in Fig. 1 ) and were downloaded using scripts based on the PlanetNICFI R package v1. 0. 3  (Mouselimis, 2022). Data acquisitions encompassed the period from December 2015 to March 2022. This corresponded to 10 bi-annual Planet NICFI mosaics combining the best cloud-free images in a sixmonth window from January to June and from July to December of each calendar year from 2015 to 2020.
, 2022)  the official dataset of forest degradation in the Brazilian Amazon, mapping both logged and burned forests. We manually sampled a total of 289 tiles across the Mato Grosso state for a total of 115,600 km 2 sampled area. For each tile, all degradation occurrences were vectorized as polygons encompassing the whole degraded area and not only the individual spatial features of each disturbance type (e. g. , isolated pixels of exposed ground at logged forests). Samples were collected and reviewed by three experts in degradation photo interpretation. From these tiles, we gridded the data into smaller image patches (256 × 256 pixels) and retrieved only those that intersected the presence of degradation in the manually delineated samples (n = 66,048 patches). To tackle heavy atmospheric effects identified in some bi-annual mosaics that were not present in August 2021 (e.
(2023). The cloud cover mask consisted of binary masks of cloud/non-cloud based on Planet NICFI data and a U-Net model with the same architecture used in the study, showing 91. 5% accuracy and 0. 77 F 1 -score on an independent test dataset. Cloud shadow was not considered for this filtering, because we did not identify degradation false positives associated with it. This complementary model and dataset were also developed and validated in this study and presented in Supplementary Material S1 as well as Supplementary Figs. S3-S5  for visual examples of the cloud cover mask results.
DL-DEGRAD product The DL-DEGRAD product included three different layers (maps): biannual degradation, cumulative degradation, and detection frequency. The basic data output from the modelling approach consisted of binary degradation maps, which have a value of 1 for presence and 0 for absence of degradation, created for each disturbance type independently (logging, fire, and roads). For the period where we had monthly mosaics, we combined them into bi-annual degradation maps (months from January to June and from July to December) by taking the sum of all months within the 6-month periods, independently for each disturbance type. To reduce false positives in the dataset, we confirmed predictions from all bi-annual maps if (i) they had at least two detections in the 6month window, or (ii) if they had one detection in the 6-month window, but also showed at least another detection within the three previous or next predictions, that is, a persistent signal.
, 2014). This reference dataset was also used to evaluate the relative performance of our method in comparison to existing datasets of forest disturbance: DETER, SIMEX, Global Forest Change (GFC), Tropical Moist Forests (TMF), Global Fire Loss (FireGFL), FireCCI, MCD64, MapBiomas Fire, and GABAM. In order to estimate accuracies using the same reference dataset obtained from the map strata of DL-DEGRAD, we employed the method by  Stehman (2014)  that was designed for strata being different from the assessed map classes. The calculations were done using the 'mapaccuracy' R-package  (Costa, 2022). For this analysis we filtered areas of non-forest from the compared data products. A full description of these datasets and pre-processing is presented in Supplementary Material S2 and Supplementary Table  S1.
Degradation area and frequency in Mato Grosso Finally, the degradation was analyzed across the study area regarding area and frequency of each disturbance. Degradation data were aggregated into the 20 × 20 km tiles before calculating the fraction of degraded forests from 2016 to 2021 normalized by the remaining forest in 2021. To retrieve remaining forest areas, we used the tree cover and deforestation dataset based on Planet NICFI data and DL model  (Wagner et al. , 2023). The frequency of degradation was analyzed by looking at degraded areas from the year 2017 from our cumulative degradation product, and by tabulating how many times areas were found as degraded afterwards using our frequency of degradation product. Total degraded forest areas and deforestation per year for Mato Grosso were calculated from 2017 to 2021.
0 and 59. 1) and moderate for the date (WF 1 -score values of 52. 7 and 38. 0). The remaining datasets showed moderate to weak agreement with the fire reference data showing F 1 -scores ranging from 8. 6 to 34. 3. The DL-DEGRAD road mapping had moderate agreement for location (F 1score = 38. 4) and weak for date (WF 1 -score = 23. 8). Confusion matrices, accuracies and area estimates with confidence intervals are found in Supplementary Materials S3 and Supplementary Tables from S2 to S9. By inspecting the user and producer's accuracies of products with highest performances, we observed that DL-DEGRAD results for logging and fire showed high producer's accuracy considering only location (PAs of 86. 2 and 89. 5%, respectively), and moderate user's accuracy (UAs of 57. 4 and 65. 5%, respectively).
This means that most of the degradation was found by the model (omission errors of 14% and 10% for logging and fire, respectively), but additional areas not belonging to the disturbance classes were mapped as disturbance (commission errors of 43 and 34% for logging and fire, respectively). The opposite was found for the other data products, where UA was higher than PA, indicating the products could not reliably detect most disturbances in the reference data, but their detections were generally correct when they did map it. For instance, SIMEX had the highest UA for logging (80. 9%) and DETER the highest UA for fire (87%). On the other hand, the two datasets showed lower PAs of 60. 7% and 75. 7%, respectively, indicating they omitted from 24 to 39% of the degradation events. To understand better these commission and omission errors, we inspected them one by one and found that in the case of logging as mapped by SIMEX, 59% of samples with omission were small-scale logging activities without road infrastructure (with bare soil showing on the trails), 19% were logged forests with very clear patterns of road infrastructure and likely large enough to be observed in Landsat imagery, 13% were simple human interpretation errors when drawing the vectors, and 9% occurred in areas with clear logging patterns but overlap with burn scars.
In the case of the omissions of fire mapped in DETER dataset, 56% of omissions were large-scale fires being omitted (e. g. > 500 m width), errors of geolocation (displaced detections to the patterns observed in the imagery), burn scars not being mapped at logged forests (the same but inverse issue as SIMEX), or an erroneous attribution to deforestation alert or to DETER's 'degradation' mosaic class which may mix both logging and fire, 23% related to small-scale or less severe burn scars that vanish quickly from the imagery (disappear in 6-12 months), and 21% related to random errors due to the sample being close to the edge of the burn scar or the forest edge, and the polygon was off by up to 100 m (few Landsat pixels). In agreement with the results of Table  3 , we found a strong agreement between the independent test dataset and our DL-DEGRAD product when visually inspecting the degradation by logging mapped with the different products (Fig.
4A-D ). Results were also consistent with the detections of SIMEX (based on official Mato Grosso state logging concessions data) and DETER (official national degradation data from INPE). The GFC and TMF (Fig. 4L  and M ) showed scattered detections in correct locations of logged forests, depicting logging trails and decks. However, the detections by these two products did not encompass the whole boundary of the logged forests. GFC showed many detections in locations unrelated to degradation by logging, such as in the bottom-left corner of the area (Fig. 4 ). Those areas are recently burned and deforested areas. The degradation by fire in the example revealed strong agreement between our DL-DEGRAD product and the reference dataset, and some disagreement with the other products, especially regarding the disturbance year (Fig.
Spatial agreement of degradation products DL-DEGRAD mapped 77. 89% and 64. 76% of the total logged forests present in SIMEX and DETER datasets, respectively (Table  4 ). On the other hand, GFC and TMF mapped <20% of the total logging present in SIMEX and DETER. Regarding forest fire, DL-DEGRAD mapped 72. 59% of all forest fire observed in DETER, while the next highest recalls or overlaps were observed for FireCCI (44. 13%) and GFC (41. 07%) datasets. When looking at the intersection-over-union metrics between all products (Tables  5  and 6 ), meaning the intersecting area mapped by two products divided by the total area mapped by both, DL-DEGRAD also showed the highest overlap with DETER (IoU = 20. 89%) and SIMEX (IoU = 27. 87%) for logging degradation. The other data products showed a very low overlap with SIMEX or DETER (IoU close to 5%).
The novelty of the DL-DEGRAD approach We successfully developed and validated the first tropical forest degradation map based on 4. 77-m resolution Planet NICFI satellite data with detection and attribution of logging, fire, and road construction in the Brazilian Amazon forests in the state of Mato Grosso. Overall, the DL-DEGRAD approach showed 97% accuracy and 68. 9%, 75. 6% and 38. 4% F 1 -scores for logging, fire, and road construction degradation mapping according to the probability design-based accuracies estimates. Compared to existing approaches and previous results, we summarize the novelty of our methodology in three contributions: (1) Our methodology is designed to automatically map areas impacted by forest degradation (forest remaining forest by losing carbon and change of structure) with close to human level accuracy, similarly as regional official datasets as DETER and SIMEX, and not only mapping pixels with complete clearings or cover change  (Hansen et al.
, 2014). The model showed high agreement on determining the exact year of degradation, with some variability in the year-to-year user's and producer's accuracy but with no clear trend. This means that even though we trained the model mostly using only the mosaic of one date (August 2021), it was able to learn the spatial features associated with degradation and reproduce that across time and space. This is an important feature of our model, because one of the main purposes of having such a model is reducing the manual effort to produce maps at different locations and over multiple years, therefore overcoming limitations of noise in the PlanetScope data and ultimately being able to achieve mapping at large scale. We expect this ability to generalize to other locations and dates may be attributed to multiple components of the training dataset.
, 2005). However, this kind of mapping does not return area estimates close to the real extent of logged forests, which correspond to the bounding box around trees that have been logged. By mapping the area of logged forests, that is, the boundaries around trails, log decks, and treefall gaps (whenever visible), our method and dataset produce area estimates close to the extent of degraded forests. This is supported by the high overlap between DL-DEGRAD with SIMEX data, which considers logging permits for areas of forest management  (Silgueiro et al. , 2021). Small-scale logging was also captured by DL-DEGRAD, in which the occurrence of roads was not the main feature for disturbance detection (example shown in Supplementary Fig. S6 ). Second, the model also leveraged the high temporal resolution of PlanetScope imagery to map forest degradation every six months, that is, before the regeneration can cover the tracks of disturbance.
This contributed to show that logging disturbances lingered for a longer time in the imagery than the footprints from fire disturbances. On average, our frequency data indicated that logging patterns persisted in the imagery for 2. 13 years, while fire patterns persisted in the imagery for 1. 42 years. This means that at least annual datasets are required to fully capture forest degradation effects, especially those caused by fire, which is easily achievable by the PlanetScope data.
Comparison of degradation products Among the degradation products compared in the analysis, DL-DEGRAD logging and fire maps showed one of the highest F1-scores according to the design-based stratified random sampling approach used here, in relation to the correct location as well as the correct timing of degradation. The results brought by DL-DEGRAD showed similar performance to those from regional datasets of DETER and SIMEX based on visual interpretation of satellite data and manual delineation of degradation. While considering limitations of data comparison, the difference between DL-DEGRAD and SIMEX for mapping logged forests was <1% of F1-Score; and for forest fire, DETER had 5% higher F1-Score on identifying the location, but 16% less F1-Score on identifying the correct year of degradation than the DL-DEGRAD.
Moreover, a similar outcome was observed when we compared the pixel-by-pixel agreement between data products, where DL-DEGRAD showed the highest agreement with the visual-interpreted regional datasets DETER and SIMEX when compared to the other automatic datasets. This reinforces that it is not a matter of the data used for the evaluation or their spatial resolution, but that the methodology is in fact mapping forest degradation more consistently and with human-level accuracy when compared to other automatic methods. One difference, however, is that DL-DEGRAD mapped two to three times more areas of degradation than DETER and SIMEX, thereby finding areas that were not previously mapped. We note however, as shown in the results, part of these mapped areas can be attributed to commission errors from DL-DEGRAD, but also to omission errors from SIMEX and DETER.
Area estimates should be done using the design-based approach, which accommodates errors in the maps. We expect our dataset to complement these initiatives to obtain more accurate maps of forest degradation, as well as making them more efficient and consistent than visual inspection. While performing visual interpretation and manual data collection for such large areas may take months of work by trained analysts, through our model framework we can map the entire Mato Grosso rainforest area (>500,000 km 2 ) from new Planet NICFI monthly image composites in less than one day. An in-depth look into the omission errors in SIMEX for Logging (39. 3%) and DETER for Fire (24. 3%) reveals that 60 and 44% of these errors of these two datasets, respectively, are likely not avoidable when using Landsat data because they consist of (i) small-scale patterns which only appeared in one Planet NICFI bi-annual mosaic and then disappeared in the next mosaic, that is, vegetation quickly recovered in a matter of six to twelve months; and (ii) logging areas without well-built road infrastructure; or (iii) image resolution-related issues, such as omission of fire at the borders of burn scars.
On the other hand, some errors can be avoided, such as logging not being identified if the area was also burned (also the inverse), DETER geolocation errors showing detections displaced from 80 to 200 m of the actual degradation, and omitted large-scale forest fires (e. g. >500 m of width) which could be clearly seen in the Landsat imagery but were not mapped by DETER. By deducing the potentially avoidable errors (40 and 66% of logging and fire), we estimate the Landsat data would allow the observation up to a theoretical maximum of 84. 3 and 84% of all logging and fire in the reference dataset. This reveals a limitation to what Landsat data may offer for visual interpretation of degradation in the Mato Grosso region, of which shows large logging infrastructure and large forest fire events.
Thus, if the dynamics of logging in other regions involve less development of infrastructure (e. g. , illegal logging), or fires are smaller in size, the rate of detection using Landsat data will likely decrease. Nevertheless, although having higher commission errors than SIMEX and DETER a rate that can be reduced in further developments of the model, our DL approach using Planet NICFI allows to observe much more of these small-scale logging activities with low omission errors (10%). The DL-DEGRAD approach offers improved mapping abilities for logging degradation that are not matched by any other automatic data product, and that reduces the minimum area mapped by visualinterpreted datasets (DETER and SIMEX) based on 30-m satellite data. Moreover, the DL-DEGRAD product showed higher sensitivity in detecting forest fires than the compared automatic burned area products.
Besides DL-DEGRAD, the FireGFL dataset also showed good agreement for fire degradation mapping (F1-Score of 59. 1). We find that the FireGFL is an important refinement over the GFC product because it mainly retrieves forest fires reducing other sources of disturbance  (Tyukavina et al. , 2022). The remaining automatic burned area maps had low map agreements (F 1 -score lower than 34%). This low map agreement was especially observed regarding time of detection, which highly varied between burned area products, indicating the high uncertainty to map burned forests using available datasets. We expect that our methodology can contribute to surpass previous limitations of understory fire mapping sensitivities  (Morton et al. , 2011; Dupuis et al. , 2020; Gao et al. , 2020). By visual examining the Planet NICFI imagery, we identified that burned area products usually detected burned forests when the fire severity was high and barely any vegetation was left, corresponding to brown to black colors in the NICFI true color composites.
Limitations The differences in spatial and temporal resolution between the compared data products bring some potential limitations in the data analysis. Also, the accuracy metrics of the compared datasets represent the accuracy of the resampled (down-sampled) datasets and according to the degradation definitions utilized in this study. This type of comparison is complex and has intrinsic limitations as discussed in previous studies  (Tyukavina et al. , 2017; Pessôa et al. , 2020). This is the reason why an effort was made here to provide independent reference data as well as to compare products on a per-pixel basis. We note that there is also subjectivity in manual vectorization of degradation of which there is not a simple solution. The boundary of the degraded areas is usually not sharp and well defined such as a forest/non-forest boundary, and its interpretation can vary according to the interpreteras also highlighted some issues in complementary products such as SIMEX and DETER.
However, after training the DL model to perform the mapping, it can provide a more consistent and reliable approach to map the boundaries of degraded forests based on the data, thus following the spectral/ textural features of which humans may lack visual acuity to identify boundaries or manual dexterity to accurately draw them. However, the good agreement presented between our automated approach and manually delineated products (DETER and SIMEX) highlight the method is able to reproduce what other analysts also identified as degradation in these official datasets. A thorough assessment of degradation intensity and how much of it leads to a detection is yet to be assessed in future studies. For logging degradation, this means acquiring and integrating data of carbon losses in logging concessions at state and national level, however for degradation due to fire, ground data is way more limited.
S10 ). The spatial extent of areas degraded by logging and fire mapped by the DL-DEGRAD approach is higher than other methods that mostly track complete canopy clearings, but a strong spatial agreement is observed with degraded forests mapped by SIMEX and DETER datasets  (Silgueiro et al. , 2021; De Almeida et al. , 2022). These estimates differ from other datasets, such as GFC and TMF, which detect only pixels with tree loss pixels (change from forest to bare soil), explaining the much higher detected area by our method. Due to the inability to look under the canopy and to the quick gap recovery dynamics of vegetation, we expect that the exact degraded area estimate at local scales should be very hard to achieve using optical data of any spatial resolution in the future. This area could be potentially retrieved at local scales using ALS data looking at the top of the canopy and understory.
Overall, at regional scales, we expect that DL-DEGRAD has potential to capture most of the degraded forests, but some small-scale disturbances where forest can quickly recover may still be underestimated. The ability of the dataset to observe repeated degradation in the same area is still to be assessed in future research, when more data collection on repeated events is collected. During the DL training, we discovered and dealt with a few issues arising from artifacts of Planet NICFI imagery as well as from the different environments of the Amazon forests. The level of accuracy obtained was only possible because the U-Net model inherently learns the contextual information in the imagery, which minimizes issues of reflectance variability and visual artifacts present in PlanetScope data, caused by the nature of their acquisition system -a constellation of hundreds of small satellites  (Pandey et al.