This is the pre-acceptance version, to read the final version published in the journal Remote Sensing of Environment, please go to: https:// doi.org/10.1016/j.rse.2019.04.014 Landuse characterization is important for urban planning. It is traditionally performed with field surveys or manual photo interpretation, two practices that are time-consuming and labor-intensive.Therefore, we aim to automate landuse mapping at the urban-object level with a deep learning approach based on data from multiple sources (or modalities).We consider two image modalities: overhead imagery from Google Maps and ensembles of ground-based pictures (side-views) per urban-object from Google Street View (GSV). These modalities bring complementary visual information pertaining to the urban-objects. We propose an end-to-end trainable model, which uses OpenStreetMap annotations as labels. The model can accommodate a variable number of GSV pictures for the ground-based branch and can also function in the absence of ground pictures at prediction time. We test the effectiveness of our model over the area ofÎle-de-France, France, and test its generalization abilities on a set of urban-objects from the city of Nantes, France.Our proposed multimodal Convolutional Neural Network achieves considerably higher accuracies than methods that use a single image modality, making it * First Author: SS, shivangi.srivastava@wur.nl.

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We study the problem of landuse characterization at the urban-object level using deep learning algorithms. Traditionally, this task is performed by surveys or manual photo interpretation, which are expensive and difficult to update regularly. We seek to characterize usages at the single object level and to differentiate classes such as educational institutes, hospitals and religious places by visual cues contained in side-view pictures from Google Street View (GSV). These pictures provide geo-referenced information not only about the material composition of the objects but also about their actual usage, which otherwise is difficult to capture using other classical sources of data such as aerial imagery. Since the GSV database is regularly updated, this allows to consequently update the landuse maps, at lower costs than those of authoritative surveys. Because every urban-object is imaged from a number of viewpoints with street-level pictures, we propose a deeplearning based architecture that accepts arbitrary number of GSV pictures to predict the fine-grained landuse classes at the object level. These classes are taken from OpenStreetMap. A quantitative evaluation of the area of Île-de-France, France shows that our model outperforms other deep learning-based methods, making it a suitable alternative to manual landuse characterization.

This is a scientific review defining the technical feasibility of surface oxidized carbon nanotubes (CNTs) for sorption of divalent metal ions (Cd 2+ , Cu 2+ , Ni 2+ , Pb 2+ , Zn 2+ ) from aqueous solution. By conducting the detailed literature review it was found that the adsorption capacities of CNTs remarkably increased after conducting their surface oxidization with NaOCl, HNO 3 and KMnO 4 solutions. Unlike many microporous adsorbents, CNTs possess fibrous shape with high aspect ratio, large accessible external surface area, and well developed mesopores, all contribute to the superior removal capacities of these ions. The adsorption mechanisms is majorly contributed by the chemical interactions between the metal ions and the surface functional groups of the CNTs. Larger the surface area greater will be the number of reducing groups hence more attributal to better CNT sorption performances. Effective process parameters defining CNT characterizations such as surface area pore size distribution, sorbent mass, and acidity at surface, solution properties (ionic strength, pH, initial adsorbate concentration and temperature) and competition for sorption sites by multiple metal ions, governs CNTs performances which are detailed in this review. The recovery of metal ions and the regeneration of CNTs can be achieved using acid elution with little effect on the CNT performance. Often during the production of CNTs a by-product is produced known as Carbon Nano Cages. These are hollow graphitic cages similar to fullerene structures having porous morphologies, but can be multilayered and have irregular shapes unlike the traditional fullerene spheres. The adsorption of divalent ions onto the surface of CNCs works as a function of solution pH. The increase in the adsorption of divalent ions with increasing pH for CNCs suggests that an ion exchange mechanism between the H+ ions and metal ions occurs at the oxygen-containing functional groups on the surface. Process parameters for CNC characterizations mainly elaborates upon zero point charge, dispersibility of sorbent materials in aqueous media, flowability of aqueous media through sorbent materials, variation of pH and agitation time. The kinetics of metal ion adsorption by the nanocages can be very well described by a pseudo-second-order kinetics model (detailed in this paper). The utilization of CNTs & CNCs for the treatment of water and wastewater containing divalent metal ions is gaining more attention as a simple and effective means of pollution control.

OpenStreetMap (OSM) is a community-based, freely available, editable map service that was created as an alternative to authoritative ones. Given that it is edited mainly by volunteers with different mapping skills, the completeness and quality of its annotations are heterogeneous across different geographical locations. Despite that, OSM has been widely used in several applications in Geosciences, Earth Observation and environmental sciences. In this work, we present a review of recent methods based on machine learning to improve and use OSM data. Such methods aim either 1) at improving the coverage and quality of OSM layers, typically using GIS and remote sensing technologies, or 2) at using the existing OSM layers to train models based on image data to serve applications like navigation or land use classification. We believe that OSM (as well as other sources of open land maps) can change the way we interpret remote sensing data and that the synergy with machine learning can scale participatory map making and its quality to the level needed to serve global and up-to-date land mapping. A preliminary version of this manuscript has been presented in the first authors dissertation [1].

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