Historical forest biomass dynamics modelled with Landsat spectral trajectories

Acknowledgements National Forest Inventory data are available online, provided by Ministerio de Agricultura, Alimentación y Medio Ambiente (España). Landsat images are available online, provided by the USGS.Peer reviewedPostprin

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos. We utilize a recent deep-learning based video analysis framework, termed hidden two-stream networks, to recognize a range of activities in YouTube videos. This framework is efficient and can run in real time or faster which is important for recognizing events as they occur in streaming video or for reducing latency in analyzing already captured video. This is, in turn, important for using video in smart-city applications. We perform a series of experiments to show our approach is able to accurately map activities both spatially and temporally. We also demonstrate the advantages of using the visual content over the tags/titles.Comment: Accepted at ACM SIGSPATIAL 201

Segmentation of optical remote sensing images for detecting homogeneous regions in space and time.

With the amount of multitemporal and multiresolution images growing exponentially, the number of image segmentation applications is recently increasing and, simultaneously, new challenges arise. Hence, there is a need to explore new segmentation concepts and techniques that make use of the temporal dimension. This paper describes a spatio-temporal segmentation that adapts the traditional region growing technique to detect homogeneous regions in space and time in optical remote sensing images. Tests were conducted by considering the Dynamic Time Warping measure as the homogeneity criterion. Study cases on high temporal resolution for sequences of MODIS and Landsat-8 OLI vegetation indices products provided satisfactory outputs and demonstrated the potential of the spatio-temporal segmentation method.Também publicado na Revista Brasileira de Cartografia, v. 70, n. 5, p. 1779-1801, 2018. Special Issue XIX Brazilian Syposium on GeoInformatics, 2018. DOI: 10.14393/rbcv70n5-45227

Agricultural Monitoring in Regional Scale Using Clustering on Satellite Image Time Series

The remote sensing images are more accessible nowadays and there are proper technologies to receive, distribute, manipulate and process long satellite image time series that can be used to improve traditional methods for harvest monitoring and forecasting. The potential of the satellite multi-temporal images to support research of agricultural monitoring has increased according to improvements in technological development, especially in analysis of large volume of data available for knowledge discovery. In Brazil, sugarcane is cultivated on extensive fields and is the main agriculture crop used to produce ethanol. The main objective of this chapter is to monitor the sugarcane crop by clustering analysis with multi-temporal satellite images having low spatial resolution. A large database of this kind of image and specific software were used to perform the image pre-processing phase, extract time series, apply clustering method and enable the data visualization on several steps during the whole analysis process. According to the analysis done, our methodology allows to identify land areas with similar development patterns, also considering different growing seasons for the crops, covering monthly and annual periods. Results confirm that satellite images of low spatial resolution can indeed be satisfactorily used in agricultural crop monitoring in regional scale

Spatiotemporal Data Augmentation of MODIS-LANDSAT Water Bodies Using Generative Adversarial Networks

The monitoring of the shape and area of a water body is an essential component for many Earth science and Hydrological applications. For this purpose, these applications require remote sensing data which provides accurate analysis of the water bodies. In this thesis the same is being attempted, first, a model is created that can map the information from one kind of satellite that captures the data from a distance of 500m to another data that is captured by a different satellite at a distance of 30m. To achieve this, we first collected the data from both of the satellites and translated the data from one satellite to another using our proposed Hydro-GAN model. This translation gives us the accurate shape, boundary, and area of the water body. We evaluated the method by using several different similarity metrics for the area and the shape of the water body. The second part of this thesis involves augmenting the data that we obtained from the Hydro-GAN model with the original data and using this enriched data to predict the area of a water body in the future. We used the case study of Great Salt lake for this purpose. The results indicated that our proposed model was creating accurate area and shape of the water bodies. When we used our proposed model to generate data at a resolution of 30m it gave us better areal and shape accuracy. If we get more data at this resolution, we can use that data to better predict coastal lines, boundaries, as well as erosion monitoring