2,633 research outputs found
TernausNetV2: Fully Convolutional Network for Instance Segmentation
The most common approaches to instance segmentation are complex and use
two-stage networks with object proposals, conditional random-fields, template
matching or recurrent neural networks. In this work we present TernausNetV2 - a
simple fully convolutional network that allows extracting objects from a
high-resolution satellite imagery on an instance level. The network has popular
encoder-decoder type of architecture with skip connections but has a few
essential modifications that allows using for semantic as well as for instance
segmentation tasks. This approach is universal and allows to extend any network
that has been successfully applied for semantic segmentation to perform
instance segmentation task. In addition, we generalize network encoder that was
pre-trained for RGB images to use additional input channels. It makes possible
to use transfer learning from visual to a wider spectral range. For
DeepGlobe-CVPR 2018 building detection sub-challenge, based on public
leaderboard score, our approach shows superior performance in comparison to
other methods. The source code corresponding pre-trained weights are publicly
available at https://github.com/ternaus/TernausNetV
Urban land use spectral using high resolution imagery and GIS approach in sustaining urban planning spatial databases
Remote sensing technology is useful for urban planning due to its capability in examining detailed spectral characteristic of urban land uses. This study attempts to review a relevant studied have been done in identified an appropriate spectral for urban land use using high resolution remote sensing images and GIS approach. The detailed spectral for urban land uses consist of residential, industrial and commercial in metropolitan and city center urban hierarchy will be discussed. The segmentation techniques through object oriented and the use of field measurement was highlighted, at once demonstrates the usability of such infrastructure to facilitate further progress of remote sensing and GIS application in urban planning in Malaysia. Finally, a discussion of the needs for further research is presented
Assessing the role of EO in biodiversity monitoring: options for integrating in-situ observations with EO within the context of the EBONE concept
The European Biodiversity Observation Network (EBONE) is a European contribution on terrestrial monitoring to GEO BON, the Group on Earth Observations Biodiversity Observation Network. EBONE’s aims are to develop a system of biodiversity observation at regional, national and European levels by assessing existing approaches in terms of their validity and applicability starting in Europe, then expanding to regions in Africa. The objective of EBONE is to deliver:
1. A sound scientific basis for the production of statistical estimates of stock and change of key indicators;
2. The development of a system for estimating past changes and forecasting and testing policy options and management strategies for threatened ecosystems and species;
3. A proposal for a cost-effective biodiversity monitoring system.
There is a consensus that Earth Observation (EO) has a role to play in monitoring biodiversity. With its capacity to observe detailed spatial patterns and variability across large areas at regular intervals, our instinct suggests that EO could deliver the type of spatial and temporal coverage that is beyond reach with in-situ efforts. Furthermore, when considering the emerging networks of in-situ observations, the prospect of enhancing the quality of the information whilst reducing cost through integration is compelling. This report gives a realistic assessment of the role of EO in biodiversity monitoring and the options for integrating in-situ observations with EO within the context of the EBONE concept (cfr. EBONE-ID1.4). The assessment is mainly based on a set of targeted pilot studies. Building on this assessment, the report then presents a series of recommendations on the best options for using EO in an effective, consistent and sustainable biodiversity monitoring scheme.
The issues that we faced were many:
1. Integration can be interpreted in different ways. One possible interpretation is: the combined use of independent data sets to deliver a different but improved data set; another is: the use of one data set to complement another dataset.
2. The targeted improvement will vary with stakeholder group: some will seek for more efficiency, others for more reliable estimates (accuracy and/or precision); others for more detail in space and/or time or more of everything.
3. Integration requires a link between the datasets (EO and in-situ). The strength of the link between reflected electromagnetic radiation and the habitats and their biodiversity observed in-situ is function of many variables, for example: the spatial scale of the observations; timing of the observations; the adopted nomenclature for classification; the complexity of the landscape in terms of composition, spatial structure and the physical environment; the habitat and land cover types under consideration.
4. The type of the EO data available varies (function of e.g. budget, size and location of region, cloudiness, national and/or international investment in airborne campaigns or space technology) which determines its capability to deliver the required output.
EO and in-situ could be combined in different ways, depending on the type of integration we wanted to achieve and the targeted improvement. We aimed for an improvement in accuracy (i.e. the reduction in error of our indicator estimate calculated for an environmental zone). Furthermore, EO would also provide the spatial patterns for correlated in-situ data.
EBONE in its initial development, focused on three main indicators covering:
(i) the extent and change of habitats of European interest in the context of a general habitat assessment;
(ii) abundance and distribution of selected species (birds, butterflies and plants); and
(iii) fragmentation of natural and semi-natural areas.
For habitat extent, we decided that it did not matter how in-situ was integrated with EO as long as we could demonstrate that acceptable accuracies could be achieved and the precision could consistently be improved. The nomenclature used to map habitats in-situ was the General Habitat Classification. We considered the following options where the EO and in-situ play different roles:
using in-situ samples to re-calibrate a habitat map independently derived from EO; improving the accuracy of in-situ sampled habitat statistics, by post-stratification with correlated EO data; and using in-situ samples to train the classification of EO data into habitat types where the EO data delivers full coverage or a larger number of samples.
For some of the above cases we also considered the impact that the sampling strategy employed to deliver the samples would have on the accuracy and precision achieved.
Restricted access to European wide species data prevented work on the indicator ‘abundance and distribution of species’.
With respect to the indicator ‘fragmentation’, we investigated ways of delivering EO derived measures of habitat patterns that are meaningful to sampled in-situ observations
Deep Learning Models for River Classification at Sub-Meter Resolutions from Multispectral and Panchromatic Commercial Satellite Imagery
Remote sensing of the Earth's surface water is critical in a wide range of
environmental studies, from evaluating the societal impacts of seasonal
droughts and floods to the large-scale implications of climate change.
Consequently, a large literature exists on the classification of water from
satellite imagery. Yet, previous methods have been limited by 1) the spatial
resolution of public satellite imagery, 2) classification schemes that operate
at the pixel level, and 3) the need for multiple spectral bands. We advance the
state-of-the-art by 1) using commercial imagery with panchromatic and
multispectral resolutions of 30 cm and 1.2 m, respectively, 2) developing
multiple fully convolutional neural networks (FCN) that can learn the
morphological features of water bodies in addition to their spectral
properties, and 3) FCN that can classify water even from panchromatic imagery.
This study focuses on rivers in the Arctic, using images from the Quickbird,
WorldView, and GeoEye satellites. Because no training data are available at
such high resolutions, we construct those manually. First, we use the RGB, and
NIR bands of the 8-band multispectral sensors. Those trained models all achieve
excellent precision and recall over 90% on validation data, aided by on-the-fly
preprocessing of the training data specific to satellite imagery. In a novel
approach, we then use results from the multispectral model to generate training
data for FCN that only require panchromatic imagery, of which considerably more
is available. Despite the smaller feature space, these models still achieve a
precision and recall of over 85%. We provide our open-source codes and trained
model parameters to the remote sensing community, which paves the way to a wide
range of environmental hydrology applications at vastly superior accuracies and
2 orders of magnitude higher spatial resolution than previously possible.Comment: 21 pages, 10 figures, 3 table
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