dataesr/scientific-paragraphs-categorization huggingface dataset:
doi:10. 1371/journal. pone. 0147501. g001 All blind users were independent navigators, with experience in the use of a white-cane (100%) and several in the use of a guide dog as well (33%). Participants reported being familiar with GPS devices and experiencing them, but none made practical use of them or had undergone training with them. None of the blind participants had ever been previously exposed to virtual environments. All had extensive experience using the EyeMusic with static stimuli and limited experience using it in simple 2D games and in the real world as part of previous structured training in our lab (for example see  [28, 32, 40] ). This previous structured training consisted of weekly meeting with a personal instructor from the lab, similar to those we conducted with the vOICe SSD as described in  [24, 25] , and were part of a general rehabilitation program not dedicated for this experiment leading to significant differences in experience between participants.
Image processing and simplification In this experiment the images presented by the EyeMusic were only naively down-sampled without additional pre-processing such as object recognition, filters, zooming or scene simplifications. These types of algorithmic modules are being integrated more and more into new SSDs and hold the potential to significantly boost users' perception, and indeed several are in the process of integration and testing with the EyeMusic  [30]. Such processing can still be performed on the captured images from the screen without interfering with the genericity aspect as it does not require access to the environment's own code. In the current tasks users were able to perform successfully without them, but we suggest that in more complex environments such modules might be critical, and plan to use them in future work.
Acknowledgments The Authors wish to thank  Michal Rabinovits  for her help in running the experiment. This work was supported by a  European Research Council  grant (grant number  310809 ); The Charitable Gatsby Foundation; The  James S. McDonnell   Foundation Scholar Award  (grant number  220020284 ); The  Israel Science Foundation  (grant number  ISF 1684/08 ); The Edmond and  Lily Safra Center for Brain Sciences (ELSC) Vision center. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Understanding spatial patterns of land use and land cover is essential for studies addressing biodiversity, climate change and environmental modeling as well as for the design and monitoring of land use policies. The aim of this study was to create a detailed map of land use land cover of the deforested areas of the Brazilian Legal Amazon up to 2008. Deforestation data from and uses were mapped with Landsat-5/TM images analysed with techniques, such as linear spectral mixture model, threshold slicing and visual interpretation, aided by temporal information extracted from NDVI MODIS time series. The result is a high spatial resolution of land use and land cover map of the entire Brazilian Legal Amazon for the year 2008 and corresponding calculation of area occupied by different land use classes.
INTRODUCTION Over the last decades, many global scale, land use and land cover products based on coarse spatial resolution remote sensing data, have become available. For example, in the early 2000s, land cover maps at 1000 m spatial resolution such as Global Land Cover (GLC) 2000  (Bartholome et al. 2002)  and MODIS Global Land Cover  (Friedl et al. 2002)  were released. Later, higher resolution data of land cover were made available, including the MODIS collection 5 Global Land Cover  (Friedl et al. 2010 ) at 500 m spatial resolution and the GLOBCOVER 2005 and 2009 products  (Arino et al. 2007; Bontemps et al. 2010 ) at 300 m spatial resolution. While these products offered important advances in mapping global trends, applications relevant for national or regional decision-making require even finer resolution, particularly as the results of land use change analysis have been reported to differ depending on the scale of mapping.
(Rozon et al. 2015). In this work, the area under study is the Brazilian Legal Amazon. The Brazilian National Institute for Space Research (INPE) has been producing data on spatial extent of deforestation in this region since 1988 through the Program for Deforestation Monitoring in the Brazilian Legal Amazon (PRODES). TerraClass is a complement to PRODES by adding information about what was the land use and land cover spatial distribution and regional statistics in the deforested areas up to 2008. According to PRODES, deforestation in the Brazilian Amazon up to 2008 amounted to 719,710 km 2  (INPE 2015)  nearly twice the size of Germany's land area, for comparison. The scale of this deforestation has an impact on ecosystem services, biodiversity, water cycle and the carbon cycle  (Fearnside 2006 ).
Datasets This study uses a combination of deforestation vector data from PRODES  (INPE 2015)  and satellite orbital images (Landsat-5/TM, MODIS and SPOT-5). Since 1988, PRODES data has recorded the annual increment (August-July) of deforestation larger than 6. 25 ha. In 2008, total deforestation amounted to 719,210 km 2. Measured deforestation between August 2007 and July 2008 was of 11,457 km 2. This area was denoted as '2008 deforested area' and was not further classified, as we could not track its High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data ACTA AMAZONICA evolution into other land cover types using 2008 images. The PRODES data was used to provide a mask of areas for use in the subsequent land cover classification procedures.
Landsat images have been successfully used in deforestation monitoring and land use and land cover studies, due mainly to its high spatial resolution (30 m) and capacity to discriminate objects and vegetation typology  (Dubreuil et al. 2008; Bodart et al. 2011 ). This study used 214 geo-referenced Landsat-5/ TM images available from the INPE´s Image catalog (http:// www. dgi. inpe. br/CDSR/). The Landsat images are the same used to generate PRODES deforestation products. This set of images was collected mainly for the period July-September 2008 (187 scenes) when cloud cover is at its lowest in the Amazon. The Landsat images were the primary key source data for our land cover classification. Due to its high temporal resolution of 1-2 days (compared to Landsat's 16-18 days), Moderate Resolution Imaging Spectroradiometer (MODIS) images have been widely used for continuous land use and land cover mapping, despite their limited spatial resolution of 250-1000 m  (Eva et al.
MODIS NDVI temporal profiles were generated for those areas to evaluate the seasonal variation of the biomass during the growing cycle and determine best periods to obtain maximum and minimum NDVI values. Once the standard difference value was established, a classification procedure based on an IDL (Interactive High spatial resolution land use and land cover mapping of the Brazilian Legal Amazon in 2008 using Landsat-5/TM and MODIS data ACTA AMAZONICA Data Language) code  (Roerink et al. 2000)  was applied to produce a binary image with the location of the MODIS pixels classified as annual crops, named "agricultural MODIS mask". The agricultural MODIS mask was employed to choose sample areas for the supervised classification of Landsat-5/ TM images. First, image segmentation was conducted to discriminate spectrally homogeneous fields.
Segmentation was based on the Regional Growth the algorithm available in the SPRING software  (Câmara et al. 1996). It requires two parameters to aggregate pixels into homogeneous fields: the similarity and area thresholds. The similarity threshold is defined as the Euclidian distance between the grey values of two adjacent pixels or fields while the area threshold is defined as the minimum area in number of pixels to be considered as a field. There is no standard value for these thresholds as they depend on the land use spatial patterns present in each landscape. Consequently, the user has to make several approximations until a segmentation scheme is considered adequate. In our study, we found optimal similarity values between 10 and 20 grey levels and an optimal minimum area of 49 pixels giving a minimum mapping area of 4.
Fieldwork, final verification and data mosaic During our study, we collected 3,800 geo-referenced photographs, in a field mission that covered more than 10,000 km in the Brazilian Amazon. These photographs were used as support material during the interpretation process, and are publicly available on the INPE's website (www. obt. inpe. br/ fototeca/fototeca. html). At the end of the interpretation step, technicians external to the project verified the entire classification process aiming to reduce errors from divergent interpretation. The interpretation key showed in Table  2  was used in this step. The validation consisted of taking a geographically random sample of 535 points from all of the 12 categories identified. The number of sample points was determined so as to reach a statistical consistency with the level of confidence (95%) and a confidence interval of 5% for the polygon population of approximately 2.
ACKNOWLEDGMENTS The authors wish to thank the  Coordination for the Improvement of Higher Level Personnel (Capes) , for providing scholarships (Process  BEX 6083/13-3 ), UMR ESPACE-DEV by hosting the PhD project of the first author. We are thankful to  Dr's Dalton Valeriano ,  David Galbraith ,  Katia Fernandes , and to  Elinor Benami  for the many constructive comments and suggestions on the manuscript. The  World Bank , the  Brazilian Ministry of Science Technology and Innovation , and  ACTA AMAZONICA the Brazilian Ministry of the Environment  are thanked here for the funding of the  TerraClass  project. By dedication of  National Institute for Space Research (INPE)  and  Brazilian Agricultural Research Corporation  (Embrapa) technical teams.
Abstract. Fossil-fuel (FF) burning releases carbon dioxide (CO 2 ) together with many other chemical species, some of which, such as nitrogen dioxide (NO 2 ) and carbon monoxide (CO), are routinely monitored from space. This study examines the feasibility of estimation of FF CO 2 emissions from large industrial regions by using NO 2 and CO column retrievals from satellite measurements in combination with simulations by a mesoscale chemistry transport model (CTM). To this end, an inverse modeling method is developed that allows estimating FF CO 2 emissions from different sectors of the economy, as well as the total CO 2 emissions, in a given region. The key steps of the method are (1) inferring "top-down" estimates of the regional budget of anthropogenic NO x and CO emissions from satellite measurements of proxy species (NO 2 and CO in the case considered) without using formal a priori constraints on these budgets, (2) the application of emission factors (the NO xto-CO 2 and CO-to-CO 2 emission ratios in each sector) that relate FF CO 2 emissions to the proxy species emissions and are evaluated by using data of "bottom-up" emission inventories, and (3) cross-validation and optimal combination of the estimates of CO 2 emission budgets derived from measurements of the different proxy species.
Uncertainties in the top-down estimates of the NO x and CO emissions are evaluated and systematic differences between the measured and simulated data are taken into account by using original robust techniques validated with synthetic data. To examine the potential of the method, it was applied to the budget of emissions for a western European region including 12 coun-tries by using NO 2 and CO column amounts retrieved from, respectively, the OMI and IASI satellite measurements and simulated by the CHIMERE mesoscale CTM, along with the emission conversion factors based on the EDGAR v4. 2 emission inventory. The analysis was focused on evaluation of the uncertainty levels for the top-down NO x and CO emission estimates and "hybrid" estimates (that is, those based on both atmospheric measurements of a given proxy species and respective bottom-up emission inventory data) of FF CO 2 emissions, as well as on examining consistency between the FF NO 2 emission estimates derived from measurements of the different proxy species.
It is found that NO 2 measurements can provide much stronger constraints to the total annual FF CO 2 emissions in the study region than CO measurements, the accuracy of the NO 2 -measurement-based CO 2 emission estimate being mostly limited by the uncertainty in the top-down NO x emission estimate. Nonetheless, CO measurements are also found to be useful as they provide additional constraints to CO 2 emissions and enable evaluation of the hybrid FF CO 2 emission estimates obtained from NO 2 measurements. Our most reliable estimate for the total annual FF CO 2 emissions in the study region in 2008 (2. 71 ± 0. 30 Pg CO 2 ) is found to be about 11 and 5 % lower than the respective estimates based on the EDGAR v. 4. 2 (3. 03 Pg CO 2 ) and CDIAC (2. 86 Pg CO 2 ) emission inventories, with the difference between our estimate and the CDIAC inventory data not being statistically significant.
, 2006; Guan et al. , 2012; Korsbakken et al. , 2016). For example, the uncertainty of available estimates of the total FF CO 2 emissions in China is assessed to be about 15-20 %  (Gregg et al. , 2008). Much larger uncertainties may be associated with the subnational spatial distributions and temporal evolution of FF CO 2 emissions within a year  (Ciais et al. , 2010b). The uncertainties in anthropogenic CO 2 emission inventory data are mostly due to inaccuracies of available data regarding fuel consumption and fuel chemical composition. Note that the estimation of uncertainty in emission inventory data is itself a challenging task: in particular, as different inventories are usually based (at least partly) on common sources of information, their intercomparison does not necessarily result in revealing all the uncertainties.
We also had to ensure that the evaluation of confidence intervals does not involve any subjective quantitative assumptions regarding the level of uncertainties in measured and simulated data. Such requirements would be difficult to satisfy if the top-down estimates of the emission annual budgets were partly constrained (in the Bayesian sense) with a priori knowledge on these budgets from a bottom-up emission inventory (as it is usual in inverse modeling studies). Furthermore, the use of a priori constraints would make the cross-validation of the estimates of FF CO 2 emission budgets based on NO 2 and CO measurements infeasible, as both priors and cross-validation estimates could then be biased in a similar way due to possible systematic uncertainties in activity data employed in the emission inventory.
Accordingly, a distinctive feature of our method is that it does not involve any formal a priori constraints to the top-down estimates of the emission budgets or any quantitative settings specifying the level of uncertainties in measured and simulated data. This feature is expected to reinforce the potential of the method to elucidate possible uncertainties and/or inconsistencies in CO 2 , NO x , and CO emission data provided by different bottom-up emission inventories. In this study, our method is applied for estimation of annual FF CO 2 emissions from a group of 12 western European countries, including a selection of 11 member states of the European Union (EU) that provide the predominant part (> 70 %) of EU total FF carbon emissions  (Ciais et al. , 2010b)  and Switzerland. Taking into account availability of bottom-up emission inventory data necessary for our analysis, the annual emission estimates were obtained specifically for the year 2008.
, 2009; Krol et al. , 2013; Konovalov et al. , 2014). We considered the relatively high sensitivity of the IASI measurements in the lower troposphere as an important advantage, especially in the context of the given study involving a mesoscale CTM. Indeed, the upper troposphere CO content simulated with such a CTM is likely to be strongly affected by boundary conditions which are specified by using global CTM simulations and therefore are not dependent on CO emissions used in the regional CTM (see, e. g. , a discussion in  Konovalov et al. , 2011). Exploring the potential of the alternative CO data products goes beyond the scope of the given study.
CTM simulations and initial processing of the model output data In this study, the relationships between NO x and CO emissions and, respectively, NO 2 and CO column amounts are simulated by the CHIMERE CTM. CHIMERE is a three-dimensional Eulerian model designed to simulate air pollution on urban, regional, and continental scales; it allows to take into account the most important atmospheric processes (such as anthropogenic, biogenic, and fire emissions, gas-phase and heterogeneous chemistry, advection, turbulent diffusion, deep convection, dry and wet deposition) affecting the atmospheric fate of a number of reactive gaseous and aerosol species (see  Menut et al. , 2013, and references therein). The model was earlier successfully used in combination with satellite NO 2 and CO retrievals in several inverse modeling studies of NO x and CO emissions (e.
, 2001) , and several heterogeneous reactions on the surfaces of aerosol particles were taken into account as described in  Menut et al. (2013). Initial and boundary conditions for several key gaseous species responsible for the oxidation capacity of the lower atmosphere (e. g. , CO, NO, NO 2 , O 3 ,H 2 O 2 , HCHO) and aerosols were specified using monthly climatological data from LMDz-INCA global model  (Folberth et al. , 2006). A full list of these species is provided in the CHIMERE documentation available on the web site www. lmd. polytechnique. fr/chimere. An influx of other species, most of which are very reactive and short-lived (such as OH and HO 2 ), into a model domain is not specified in CHIMERE. Meteorological data were obtained from the WRF-ARW (v. 3. 6) model  (Skamarock et al.
All the simulated NO 2 and CO vertical profiles corresponding (in time and space) to any pixel that contained, respectively, the OMI and IASI measurements satisfying to the criteria specified in Sect. 2. 1 were projected into the measurement vertical grids and transformed into tropospheric NO 2 columns and total CO columns, C NO 2 m and C CO m , by applying the respective averaging kernels, A NO 2 and A CO. Specifically, the simulated NO 2 profiles were transformed as follows  (Eskes and Boersma, 2003) : C NO 2 m = A NO 2 T C NO 2 m(o) , (1) where C NO 2 m(o) are the original model outputs (partial columns) interpolated to the pressure grid of the averaging kernels up to the tropopause pressure level (specified in the measurement database). Note that in relatively rare cases (constituting less than 20 % of the total number of valid observations available for the study region and period) where the tropopause pressure was smaller than the pressure at the top of the model grid (200 hPa), the lack of the simulated data at altitudes exceeding the height of the upper model layer could result in some underestimation of the modeled tropospheric columns, but such a minor inconsistency between the modeled and simulated NO 2 columns is not expected to result in underestimation of NO x emissions in our analysis, owing to application of a debiasing technique described in Sect.
Emission inventory data We used annual anthropogenic emission data for the year 2008 from several sources: the European Monitoring and Evaluation Programme (EMEP) regional emission inventory (EMEP/CEIP, 2014;  Mareckova et al. , 2014) , the Emission Database for Global Atmospheric Research, version 4. 2 (EDGAR v4. 2) (EC-JRC/PBL, 2011), and the Carbon Dioxide Information Analysis Center (CDIAC)  (Boden et al. , 2011). The EMEP inventory data were used in our simulations described in Sect. 2. 2, and the EDGAR v. 4. 2 data were used to relate the emission estimates for the proxy species with CO 2 emissions (see  Sect. 3. 3). The CDIAC data were involved in the analysis of uncertainties in our emission estimates (see Sect. 3. 4) along with the EMEP and EDGAR v4. 2 data. The EMEP/CEIP inventory is based on emission data reported under the Convention on Long-range Transboundary Air Pollution by individual countries in Europe and in the Middle East, which are expected to use a unified approach (EMEP/EEA, 2013) applicable on the national level.
In this study, we used the EMEP anthropogenic annual emission data distributed among 11 Selected Nomenclature for Air Pollutants (SNAP) sectors and provided for several pollutants, such as NO x , CO, non-methane hydrocarbons (NMHC), SO x , and particulate matter, on a grid with the resolution of 0. 5 • × 0. 5 •. Note that the EMEP inventory does not provide data for CO 2 emissions and that the emissions for the 11th sector (comprising biogenic sources and fires associated with human activities) were replaced in our simulations with data of dedicated inventories (as described in this section below). The EDGAR v4. 2 inventory is created by using the energy activity data provided by the International energy agency (IEA, 2010) and by following the methodology and fuelspecific emission factors based on the 2006 IPCC guidelines  (IPCC, 2006).
The IEA data were compiled following harmonized definitions of fuels and activities and applying the same methodologies across most countries (and some groups of countries outside of the study region). We used the EDGAR v4. 2 data for the national totals of anthropogenic NO x , CO, and CO 2 emissions distributed between several emission sectors (not necessarily coinciding with the SNAP sectors). Note that we used the EDGAR v4. 2 FF CO 2 emission data excluding CO 2 emissions from biofuel burning (that is, the data used were calculated after "excluding short-cycle organic carbon"), while the corresponding CO and NO x data included emissions from both fossil-fuel and biofuel burning. The FF CO 2 emission data provided by CDIAC are based on the energy statistics that were compiled primarily from the annual energy questionnaire distributed by the United Nations Statistics Division and supplemented by official national statistical publications (UN, 2012).
The quantity of fuel was converted into the quantity of CO 2 emissions by using the methodology based on  Marland and Rotty (1984). The CDIAC database used in this study reports only national totals of FF CO 2 emissions without sectorial breakdowns and was used in this study for evaluation of uncertainties in our results. Note that CO 2 emissions from cement production have been reported in CDIAC (as well as in EDGAR v4. 2) separately from FF CO 2 emissions and were not considered in our study. Excluding this emission source from our estimates seems to be reasonable, taking into account that cement production, unlike FF burning, is not associated with considerable emissions of either NO x or CO, and so satellite measurements of the corresponding proxy species cannot provide strong constraints on CO 2 emissions from cement production.
Although satisfying this criterion alone cannot guarantee that the problem is well conditioned, the numerical experiments presented below in Sect. 3. 5 indicate that errors in our emission estimates due to probable errors in input data remain limited and thus the problem considered in this study is not ill conditioned. The dimension of the control vector (one or two) is much smaller, in our case, than that of the measurement vector (including tens of thousands of observations) because we do not attempt improving the allocation of the emissions in space and time: the vectors a s c are assumed to be known (in practice, a s c are provided implicitly by an emission interface in a CTM). Similar assumptions are not unusual in inverse modeling studies involving CTMs (e. g. ,  Pétron et al. , 2004; Müller and Stavrakou, 2005; Huneeus et al.