Fine-grained population estimation is important for several domains, such as urban planning, public health, and humanitarian action. Due to limited resources, population maps with sufficient spatial resolution and temporal frequency are not available for many developing countries. Population estimates are often based on statistics available at the national or provincial level, which moreover are updated infrequently. The United Nations produce estimates of population growth rates, which are widely used to project population numbers to a target year, starting from the most recent census data. However, they use a simplified model of population growth with uniform growth rates for all urban, respectively rural areas within a country. This neglects the complex dynamics of population growth (e.g., growth rates in big cities are usually larger than in smaller urban areas) and leads to significant errors in population projections. In this work, we propose a methodology to estimate fine-grained population growth rates and present experimental results for Mozambique.

Coconut tree plantations are one of the main sources of income in several South Pacific countries. Thus, keeping track of the location of coconut trees is important for monitoring and post-disaster assessment. Although deep learning based object detectors can attain considerably accurate results, it is inevitable that errors will remain in the predictions obtained for a large test set. Since every mistake counts, in this work we propose a methodology to efficiently use the time of human annotators to find and correct a large part of erroneous coconut tree detections. We propose to use a Random forest classifer that finds detection errors to sort the regions of the image (tiles) in decreasing order of likeliness to have detection errors. In our experiments involving UAV images in the Kingdom of Tonga, the user could analyze only 24% of the tiles and correct approximatively 71% of the errors thanks to the sorting.

John E. Vargas Muñoza*, Devis Tuiab & Alexandre X. Falcãoaa Laboratory of Image Data Science, Institute of Computing University of Campinas, Campinas, Brazilb Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Wageningen, The NetherlandsJohn E. Vargas Muñoz received the B.Sc. degree in informatics engineering from the National University of San Antonio Abad in Cusco, Cusco, Peru, in 2010, and the master's degree in computer science from the University of Campinas, Campinas, Brazil, in 2015. During 2017-2018, he worked on applications of machine learning to open geographical data, as a visiting Ph.D. student, in the Laboratory of Geo-information Science and Remote Sensing at Wageningen University, the Netherlands. In 2019, he received a Ph.D. in computer science from the University of Campinas, Campinas, Brazil. His research interests include machine learning, image processing, remote sensing image classification, and crowdsourced geographic information analysis.Devis Tuia (S'07, M’09, SM’15) received the Ph.D in environmental sciences at the University of Lausanne, Switzerland, in 2009. He was a Postdoc at the University of Valencia, the University of Colorado, Boulder, CO and EPFL Lausanne. Between 2014 and 2017, he was Assistant Professor at the University of Zurich. He is now Full Professor at the Geo-Information Science and Remote Sensing Laboratory at Wageningen University, the Netherlands. He is interested in algorithms for information extraction and data fusion of geospatial data (including remote sensing) using machine learning and computer vision. He serves as Associate Editor for IEEE TGRS and the Journal of the ISPRS. More info on http://devis.tuia.googlepages.com/Alexandre X. Falcão is full professor at the Institute of Computing, University of Campinas, Campinas, SP, Brazil. He received a B.Sc. in Electrical Engineering from the Federal University of Pernambuco, Recife, PE, Brazil, in 1988. He has worked in biomedical image processing, visualization and analysis since 1991. In 1993, he received a M.Sc. in Electrical Engineering from the University of Campinas, Campinas, SP, Brazil. During 1994-1996, he worked with the Medical Image Processing Group at the Department of Radiology, University of Pennsylvania, PA, USA, on interactive image segmentation for his doctorate. He got his doctorate in Electrical Engineering from the University of Campinas in 1996. In 1997, he worked in a project for Globo TV at a research center, CPqD-TELEBRAS in Campinas, developing methods for video quality assessment. His experience as professor of Computer Science and Engineering started in 1998 at the University of Campinas. His main research interests include image/video processing, visualization, and analysis; graph algorithms and dynamic programming; image annotation, organization, and retrieval; machine learning and pattern recognition; and image analysis applications in Biology, Medicine, Biometrics, Geology, and Agriculture.CONTACT John E. Vargas Muñoz [email protected] populations in rural areas of developing countries has attracted the attention of humanitarian mapping projects since it is important to plan actions that affect vulnerable areas. Recent efforts have tackled this problem as the detection of buildings in aerial images. However, the quality and the amount of rural building annotated data in open mapping services like OpenStreetMap (OSM) is not sufficient for training accurate models for such detection. Although these methods have the potential of aiding in the update of rural building information, they are not accurate enough to automatically update the rural building maps. In this paper, we explore a human-computer interaction approach and propose an interactive method to support and optimize the work of volunteers in OSM. The user is asked to verify/correct the annotation of selected tiles during several iterations and therefore improving the model with the new annotated data. The experimental results, with simulated and real user annotation corrections, show that the proposed method greatly reduces the amount of data that the volunteers of OSM need to verify/correct. The proposed methodology could benefit humanitarian mapping projects, not only by making more efficient the process of annotation but also by improving the engagement of volunteers.

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 deep-learning 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.

The detection and counting of coconut trees in aerial images are important tasks for environment monitoring and post-disaster assessment. Recent deep-learning-based methods can attain accurate results, but they require a reasonably high number of annotated training samples. In order to obtain such large training sets with considerably reduced human effort, we present a semi-automatic sample annotation method based on the 2D t-SNE projection of the sample feature space. The proposed approach can facilitate the construction of effective training sets more efficiently than using the traditional manual annotation, as shown in our experimental results with VHR images from the Kingdom of Tonga.

Superpixel segmentation has emerged as an important research problem in the areas of image processing and computer vision. In this paper, we propose a framework, namely Iterative Spanning Forest (ISF), in which improved sets of connected superpixels (supervoxels in 3D) can be generated by a sequence of image foresting transforms. In this framework, one can choose the most suitable combination of ISF components for a given application-i.e., 1) a seed sampling strategy; 2) a connectivity function; 3) an adjacency relation; and 4) a seed pixel recomputation procedure. The superpixels in ISF structurally correspond to spanning trees rooted at those seeds. We present five ISF-based methods to illustrate different choices for those components. These methods are compared with a number of state-of-the-art approaches with respect to effectiveness and efficiency. Experiments are carried out on several datasets containing 2D and 3D objects with distinct texture and shape properties, including a high-level application, named sky image segmentation. The theoretical properties of ISF are demonstrated in the supplementary material and the results show ISF-based methods rank consistently among the best for all datasets.

Mapping rural buildings in developing countries is crucial to monitor and plan in those vulnerable areas. Despite the existence of some rural building annotations in OpenStreetMap (OSM), those are of insufficient quantity and quality to train models able to map large areas accurately. In particular, these annotations are very often misaligned with respect to the buildings that are present in updated aerial imagery. We propose a Markov Random Field (MRF) method to correct misaligned rural building annotations. To do so, our method uses i) the correlation between candidate aligned OSM annotations and buildings roughly detected on aerial images and ii) the local consistency of the alignment vectors.

In this paper, we propose a post classification smoothing method aimed at improving the accuracy and visual appearance of sub-decimeter image classification results. Starting from the class confidence maps of a supervised classifier, we find a set of high confidence markers and propagate labels on an extended region adjacency graph. We apply the proposed method on a challenging 5cm resolution dataset over Potsdam, Germany. The proposed algorithm outperforms state-of-the-art post classification smoothing algorithms both when the classifier is trained specifically on the image and when it is trained and tested in different set of images.

The detection of copy-move image tampering is of paramount importance nowadays, mainly due to its potential use for misleading the opinion forming process of the general public. In this paper, we go beyond traditional forgery detectors and aim at combining different properties of copy-move detection approaches by modeling the problem on a multiscale behavior knowledge space, which encodes the output combinations of different techniques as a priori probabilities considering multiple scales of the training data. Afterward, the conditional probabilities missing entries are properly estimated through generative models applied on the existing training data. Finally, we propose different techniques that exploit the multi-directionality of the data to generate the final outcome detection map in a machine learning decision-making fashion. Experimental results on complex data sets, comparing the proposed techniques with a gamut of copy-move detection approaches and other fusion methodologies in the literature, show the effectiveness of the proposed method and its suitability for real-world applications.