From big data to big information and big knowledge: The case of Earth observation data

Some particularly important rich sources of open and free big geospatial data are the Earth observation (EO) programs of various countries such as the Landsat program of the US and the Copernicus programme of the European Union. EO data is a paradigmatic case of big data and the same is true for the big information and big knowledge extracted from it. EO data (satellite images and in-situ data), and the information and knowledge extracted from it, can be utilized in many applications with financial and environmental impact in areas such as emergency management, climate change, agriculture and security

Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution

Self-driving cars need to understand 3D scenes efficiently and accurately in order to drive safely. Given the limited hardware resources, existing 3D perception models are not able to recognize small instances (e.g., pedestrians, cyclists) very well due to the low-resolution voxelization and aggressive downsampling. To this end, we propose Sparse Point-Voxel Convolution (SPVConv), a lightweight 3D module that equips the vanilla Sparse Convolution with the high-resolution point-based branch. With negligible overhead, this point-based branch is able to preserve the fine details even from large outdoor scenes. To explore the spectrum of efficient 3D models, we first define a flexible architecture design space based on SPVConv, and we then present 3D Neural Architecture Search (3D-NAS) to search the optimal network architecture over this diverse design space efficiently and effectively. Experimental results validate that the resulting SPVNAS model is fast and accurate: it outperforms the state-of-the-art MinkowskiNet by 3.3%, ranking 1st on the competitive SemanticKITTI leaderboard. It also achieves 8x computation reduction and 3x measured speedup over MinkowskiNet with higher accuracy. Finally, we transfer our method to 3D object detection, and it achieves consistent improvements over the one-stage detection baseline on KITTI.Comment: ECCV 2020. The first two authors contributed equally to this work. Project page: http://spvnas.mit.edu

Mineral chemistry and formation of awaruite and heazlewoodite in the Xerolivado chrome mine, Vourinos, Greece

The Serpentinite between the chromite bodies 4 and 5 of Xerolivado mine (Vourinos,Greece), contains sparselyvery small grains (<20\u3bcm) of awaruite (Fe0.91Cu0.06Co0.03Ni3) heazlewoodite (Ni2.91Fe0.06S2) magnetite and Copentlandite (Ni3.79Fe2.98Co2.38S8). The olivine contains 0.40 wt% NiO and 6.91 wt% FeO, while the serpentine 0.18 wt% NiO and 3.02 wt% FeO. The Co-content of awaruite is 1.31 wt% and that of heazlewoodite 0.12 wt%. Heazlewoodite is a product of the primary Copentlandite reduction, resulting from the serpentinization of the ultramafic rock. The Ni content of awaruite is derived both from olivine and from Copentlandite. The reducing environment resulting from serpentinization and the low sulphur fugacity, favour the formation of awaruite, heazlewoodite and magnetite

Chromite Grain Diameter (CGD) from the ore-hosting dunite of the Xerolivado-Skoumtsa chrome mine (Vourinos,Western Macedonia, Greece): implications for chrome ore exploration

The largest chrome-ore bearing dunite of the Vourinos Complex occurs within the Xerolivado-Skoumtsa Mine District. The host dunite body has a surface exposure of 3 km2 and extends at least 400m into the subsurface. The dunite body is hosted by harzburgite tectonite interfolded with the dunite body during ductile phases of deformation. The Xerolivado-Skoumtsa mine is one of the world\u2019s largest ophiolite-hosted chrome deposits with a potential of 6 million tons of ore assaying at 22 wt.% Cr2O3. Even so, chrome ore bodies compose less than 1% of the volume of the dunite body. Exploration consisted chiefly of expensive drilling programs. A well-documented suite of chrome ores from the south sector of the mine were collected during the final years of its operation (1987-1988). Samples include twenty-seven (27) samples of serpentinite altered from primary olivine that originally coexisted with the chromite. Two types of serpentinised dunite were distinguished: Type A samples are from serpentinised dunite 1m from the ore bodies, and Type B samples are silicates infolded with the ores during original high-temperature mantle deformation. Thin-polished sections were studied via optical microscopes (transmitted and reflected light). All chromite grains (2,776 total measurements) were photographed and measured in eight directions at angular resolution of 22.5o, starting from the maximum diameter. The average and median chromite grain diameter (CGD) was calculated for each sample. These geometric analyses indicate that CGD decreases depending on the position (Type A or Type B) of the samples: the average and the median of the CGD were smaller in the serpentinites next to the chromite ore bodies (Type A) than those within consecutive chromite ore bodies (Type B). A decrease of the average and median CGD was found in (today\u2019s) vertical ore dimension: samples from mine level 717m have larger average and median CGD than samples from over-lying mine level 738m and from the underlying mine level 692m. This concurs with the position of the thickest size of the ore bodies, decreasing towards the overlying levels and underlying levels. The grain size of chromite in these dunites is due to a combination processes of original Cr-spinel crystallization and grain break-up and deformation concurrent to original dunite thinning around the ore layers. With the lack of preserved olivine morphology, these structures are the only \u201cfingerprints\u201d of the type of deformation undergone on grain scale. These results could provide a \u201cpetrographic tool\u201d useful for the exploration of schlieren-type chrome ores: host dunite (serpentinite) samples present lower average and median CGD when they are found closer to the ore bodies. Future research on this subject should focus on the verification of the results in other similar ore-hosting dunites and mineralogical and/or metallogenetic interpretation of the numeric results

NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks

Deep Neural Networks (DNNs) have made significant improvements to reach the desired accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently the Google Brain's team demonstrated the ability of Capsule Networks (CapsNets) to encode and learn spatial correlations between different input features, thereby obtaining superior learning capabilities compared to traditional (i.e., non-capsule based) DNNs. However, designing CapsNets using conventional methods is a tedious job and incurs significant training effort. Recent studies have shown that powerful methods to automatically select the best/optimal DNN model configuration for a given set of applications and a training dataset are based on the Neural Architecture Search (NAS) algorithms. Moreover, due to their extreme computational and memory requirements, DNNs are employed using the specialized hardware accelerators in IoT-Edge/CPS devices. In this paper, we propose NASCaps, an automated framework for the hardware-aware NAS of different types of DNNs, covering both traditional convolutional DNNs and CapsNets. We study the efficacy of deploying a multi-objective Genetic Algorithm (e.g., based on the NSGA-II algorithm). The proposed framework can jointly optimize the network accuracy and the corresponding hardware efficiency, expressed in terms of energy, memory, and latency of a given hardware accelerator executing the DNN inference. Besides supporting the traditional DNN layers, our framework is the first to model and supports the specialized capsule layers and dynamic routing in the NAS-flow. We evaluate our framework on different datasets, generating different network configurations, and demonstrate the tradeoffs between the different output metrics. We will open-source the complete framework and configurations of the Pareto-optimal architectures at https://github.com/ehw-fit/nascaps.Comment: To appear at the IEEE/ACM International Conference on Computer-Aided Design (ICCAD '20), November 2-5, 2020, Virtual Event, US

Managing big, linked, and open earth-observation data: Using the TELEIOS/LEO software stack

Big Earth-observation (EO) data that are made freely available by space agencies come from various archives. Therefore, users trying to develop an application need to search within these archives, discover the needed data, and integrate them into their application. In this article, we argue that if EO data are published using the linked data paradigm, then the data discovery, data integration, and development of applications becomes easier. We present the life cycle of big, linked, and open EO data and show how to support their various stages using the software stack developed by the European Union (EU) research projects TELEIOS and the Linked Open EO Data for Precision Farming (LEO). We also show how this stack of tools can be used to implement an operational wildfire-monitoring service

PALAEOSEISMOLOGICAL INVESTIGATION OF THE GYRTONI FAULT (THESSALY, CENTRAL GREECE)

Two paleoseismological trenches were excavated across the Gyrtoni Fault in NE Thessaly and studied in order to understand the recent seismotectonic behavior of this structure. Twenty five fluvial-colluvial sediment and pottery samples from both the upthrown and the downthrown fault blocks were investigated. Optically Stimulated Luminescence (OSL) dating has been applied to date both sedimentary depositspalaeosoils and pottery fragments. Paleoseismological analysis of the two trenches indicates evidence of three surface faulting events in the time span between 1.42 ±0.06 ka and 5.59 ± 0.13 ka. The observed vertical displacement per event of ~0.50 m corresponds to an Mw 6.5 ± 0.1 earthquake. An average fault slip rate of 0.41 ± 0.01 mm/yr and an average recurrence of 1.39 ± 0.14 ka for earthquakes were estimated. The results documented the activity of the fault and since the return period from the most recent event (minimum age 1.42 ± 0.06 ka) has expired, the possibility for reactivation of this active structure in the near future should be included in Seismic Hazard Assessment